A Human-Inspired Control Strategy for Improving Seamless Robot-To-Human Handovers

applied sciences

Article A Human-Inspired Control Strategy for Improving Seamless Robot-To-Human Handovers

Paramin Neranon * and Tanapong Sutiphotinun

Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla 90112, Thailand; [email protected] * Correspondence: [email protected]

Abstract: One of the challenging aspects of robotics research is to successfully establish a human- like behavioural control strategy for human–robot handover, since a robotic controller is further complicated by the dynamic nature of the human response. This paper consequently highlights the development of an appropriate set of behaviour-based control for robot-to-human object handover by ﬁrst understanding an equivalent human–human handover. The optimized hybrid position and impedance control was implemented to ensure good stability, adaptability and comfort of the robot in the object handover tasks. Moreover, a questionnaire technique was employed to gather information from the participants concerning their evaluations of the developed control system. The results demonstrate that the quantitative measurement of performance of the human-inspired control strategy can be considered acceptable for seamless human–robot handovers. This also provided signiﬁcant satisfaction with the overall control performance in the robotic control system, in which the robot can dexterously pass the object to the receiver in a timely and natural manner without the

risk of harm or injury by the robot. Furthermore, the survey responses were in agreement with the parallel test outcomes, demonstrating signiﬁcant satisfaction with the overall performance of the

Citation: Neranon, P.; Sutiphotinun, robot–human interaction, as measured by an average rating of 4.20 on a ﬁve-point scale. T. A Human-Inspired Control Strategy for Improving Seamless Keywords: object handover; robot-to-human object handover; human–robot interaction; human– Robot-To-Human Handovers. Appl. human interaction; hybrid position/force control; impedance control; human-like behavioural control Sci. 2021, 11, 4437. https://doi.org/ 10.3390/app11104437

Academic Editor: Antonio 1. Introduction Fernández-Caballero Recently, robots have been widely developed over the last decades to meet the require- ments of improving human–robot interaction (HRI). HRI is a ﬁeld of study to understand, Received: 20 April 2021 design and evaluate robotic systems, while interacting with humans [1]. The interaction in- Accepted: 10 May 2021 Published: 13 May 2021 volves several communication forms and can be mainly categorized into two types: remote interaction (i.e., search and rescue [2–4], military and police [5,6], space exploration [7–9],

Publisher’s Note: MDPI stays neutral etc.) and proximate interaction (i.e., assistive robotics and educational robotics [10–14], with regard to jurisdictional claims in home or industry [15,16], etc.). published maps and institutional affil- In this research, the field of social robotics and human–robot interaction are high- iations. lighted, in which robots are being developed to serve in human assistive capacities. One of the main challenges in close proximity interaction is to efficiently establish a framework for seamless human–robot handovers (HRH). This will allow a human to act jointly with a robot safely and reliably. The researchers [17] claimed that understanding of behavioural control strategies of human–human interaction (HHI) is fundamental to design an effec- Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. tive HRI system. In addition, Neranon et al. [18] also addressed that understanding the This article is an open access article principle of human haptic interaction, when two humans work together in a joint effort to distributed under the terms and complete a shared task, is crucial in designing an effective HRI system. conditions of the Creative Commons This section emphasizes the background of handover-based research, since human– Attribution (CC BY) license (https:// human handovers (HHH) are the ultimate benchmark for seamless handovers. A giver is creativecommons.org/licenses/by/ defined as an agent who holds an object and passes it to another. In the physical interaction, 4.0/). a receiver is a particular position that receives the object by starting pulling it. Afterwards,

Appl. Sci. 2021, 11, 4437. https://doi.org/10.3390/app11104437 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 4437 2 of 19

the receiver takes responsibility for the object throughout the transferring process [19,20]. Much of the relevant HHI research, which has steadily increased to enhance the understanding of HHI behaviour, has been reviewed. Previous studies have examined issues regarding investigating the control characteristics of two humans in a physical handover task, for examples, analysing (1) the influence of velocity [21,22] and trajectory [23,24] on the handover movements, (2) grip forces and load forces in the object exchange [25], (3) grasp points [26,27] and locations [28,29] and finally, (4) configuration [30,31] of the exchanged object. These can be used as conceptual frameworks for designing control systems for cooperative robots to work with other partners by imitating human-based behaviour control strategies. Sutiphotinun et al. [32] investigated the human behavioural responses in HHH tasks under various conditions. The paradigm findings show human dynamic characteristics, i.e., interactive force analysis, suitable transfer locations between the givers and receivers and the handover sequences. The handover is distinctively categorized into sending- transferring-receiving phases, where the giver agent primarily decides to release the object. This will be useful for developing a robotic behaviour-based approach in seamless HRH in future. Saki Kato et.al [33] analysed how humans (givers and receivers) determine the locations of object transfer during handovers for a good understanding of the mechanisms of human social behaviour. The study also presented a linear model with the influential factors affecting the handover locations. It consists of participant heights, genders and social dominance of the two partners and the distance between both humans. Chien et al. [34] proposed adaptive coordination strategies for physical HRH. It was based on the identification of HHH strategies regarding how people adapt their handover actions to the workload of their partners. The newly developed approach involving two baselines, namely, (1) proactive and (2) reactive coordination techniques, enabled similarly fluid and effective HRH tasks. Sisbot et al. [35] proposed a robotic navigation planner and a manipulation planning framework by first understanding the human dynamic characteristics of the haptic interaction. This facilitated the robot to autonomously select the best handover location based on context (i.e., accessibility, posture, a field of view and human’s safety etc.). This offered safe, legible and socially acceptable paths for HRH tasks. Some researchers [36–38] examined a robotic mechanism guideline to allow the robot to grasp an object based on human-like reaching gestures before handing it over to a human partner. It involved the object’s shape and function and the minimization of the risk of an injury occurring during the HRI execution. Jae-Bong et al. [39] developed a general-purpose software framework for a Human support robot (HSR) service robot. It directly communicated with a human using complex speech commands. This allowed the robot to move smoothly in a confined space and also manipulate various household objects and autonomously pass them to a human. As exten- sively reviewed, the existing research on the development of HRH has almost underlined the investigation on behavioural strategies and the establishment of a framework for a robotic behaviour-based approach. This allowed the robot to approach a person, reach over an object and hand over it with human-like configurations and motions. However, there has been little discussion on the crucial challenges of the development of seamless HRI, since a robotic control system is further complicated by the dynamic nature of the human environment. Thus, it necessitates a very careful design and implementation of optimized behavioural control strategies to significantly improve the success rate of effective object handover tasks. This also protects a human partner from the risk of harm or injury by a robot in the cooperative tasks. To solve the limitations as mentioned, therefore, this paper presents the design of a human-inspired control strategy for improving seamless robot–human handovers using the hybrid PID position control and impedance control algorithm. This system will enhance the accuracy of positioning and force regulating abilities in the HRH system and also increase the smoothness of human–robot physical interaction. Appl. Sci. 2021, 11, 4437 3 of 19

This paper details an optimized robotic control implementation for HRH by first understanding haptic dynamic interaction in HHH tasks in terms of interactive force, suitable transfer locations between the givers and receivers and the handover sequences. This knowledge is then used to design a robotic behaviour-based approach, which is the crucial aspect of the development of effective HRH. It is followed by how to develop optimized hybrid position/force control involving PID position control and force impedance control. The robotic control incorporates feedback signals from the HSR-3D-depth sensor, encoders (joint estimators) and multi-axis force/torque sensor attached to the robot wrist joint, respectively. The dynamic mechanical model of the physical HRH was additionally explained. Afterwards, the final sections express the control system evaluations, experimental results of the robotic HRH tasks and finally, conclusion.

2. Design of the Robotic Human-Inspired Control Strategy for HRH 2.1. Background of Human-Human Handover Strategy It is crucial to understand the kinematics and dynamics of HHH behaviour to design a conceptual guideline for a robotic human-like controller for robust, behaviour-based, HRH. Consequently, a set of HHH tests was initially conducted as fully detailed in our previous journal article [32]. It can be summarized that the experimental results of HHH intensively addressed (1) investigation of the human handover strategies of the givers and receivers (2) examination of the interactive forces and the transfer locations between the couple and (3) clariﬁcation of the mathematical model of the giver’s arm, which leads to a better understanding how the giver regulates the bilateral force before releasing the object to be transferred to the receiver. The tests give the behavioural explanation of HHH in which the HHH process can be interpreted as three distinct phases consisting of (1) sending, (2) transferring and (3) receiving. In the sending session, the giver starts grasping and holding an object and moves to the receiver, who is in the ﬁeld of the giver’s view. Once the handler recognizes a receiver and his/her position, the giver subsequently computes and estimates a suitable object transfer point based on human-relevant parameters. In the meantime, the giver is then moving to the transfer point facing the receiver before naturally handling the object. The handover locations y (height from the ground) is formulated as Equation (1), where x1 and x2 are the giver’s and receiver’s heights, respectively. Contrastingly, the transfer distance (in the x-axis) along the facing direction was approximately 0.85 m.

2 2 y = 8.23 + 0.243x1 − 9.592x2 − 1.626x1 + 1.441x2 + 3.189x1x2 (1)

In the transferring phase, the giver’s decision is based on the amount of threshold (interactive) force ( ftsh), which was signiﬁcantly proportional to the object mass. The relationship between the ftsh as a function of object mass (ϑm) can be exposed in Equation (2).

3 2 ftsh = −85ϑm + 100.75ϑm − 35.78ϑm + 6.75 (2)

The handover time was approximately 0.46 s. Moreover, to gain more understanding of how the giver regulates the interactive force, the transfer function (TG) of the giver’s hand can be expressed as the following equation:

−2.63 × 10−4S + 5211 T = (3) G S2 − 20.25S + 51.76 These ﬁndings disclose the human characteristics of the haptic handover interaction and for the aim of obtaining a conceptual guideline for a robotic human-like control strategy in HRH. Appl. Sci. 2021, 11, x FOR PEER REVIEW 4 of 20 Appl. Sci. 2021, 11, 4437 4 of 19

2.2. Toyota HSR Platform 2.2. Toyota HSR Platform An HSR home service as depicts in Figure 1a has been launched by the Toyota Corp. the robotAn HSRcan be home controlled service intuitively as depicts inusing Figure voice1a hascommands been launched or a touchscreen by the Toyota graphical Corp. userthe robot interface. can be The controlled core technologies intuitively usingof the voice HSR commands comprise speech or a touchscreen recognition, graphical object recuserognition, interface. autonomous The core navigation technologies and of localization, the HSR comprise motion planning, speech recognition,task planning, object etc. Theserecognition, are adequate autonomous to develop navigation HRH in and this localization,research. It is motion realizing planning, that the tasksafety planning, issue is anetc. essential These areaspect adequate of a robot to develop to collaborate HRH in with this humans research. in Ita issafe realizing and natu thatral the manner. safety Obstacleissue is anavoidance essential and aspect a force of arelaxation robot to collaboratemechanism withwere humansemployed in to a safeprotect and humans natural frommanner. any Obstaclemistakes avoidanceor accidents. and As a the force robot relaxation has to accommodate mechanism were a wide employed variety of to house- protect holdhumans activities, from any then mistakes its cylindrical or accidents. telescoping As the robot body has was to accommodate originally fab aricated. wide variety It was of household activities, then its cylindrical telescoping body was originally fabricated. It was equipped with a differential pseudo-omnidirectional wheel and a central yaw joint which equipped with a differential pseudo-omnidirectional wheel and a central yaw joint which allow the robot to handle most manipulation tasks with smooth changes in direction [39]. allow the robot to handle most manipulation tasks with smooth changes in direction [39]. To construct a robot map and map-based self-localization, the robot base has To construct a robot map and map-based self-localization, the robot base has equipped equipped with two types of sensors, i.e., a LIDAR sensor and an inertial measurement with two types of sensors, i.e., a LIDAR sensor and an inertial measurement unit (IMU). unit (IMU). The HSR manipulator arms are made up of a shoulder pitch joint and roll- The HSR manipulator arms are made up of a shoulder pitch joint and roll-pitch-roll wrist pitch-roll wrist joints. The robot end-effector has a two-finger parallel gripper (driven by joints. The robot end-effector has a two-finger parallel gripper (driven by a single motor), a single motor), which was furnished by a wrist multi-axis force/torque sensor and hand which was furnished by a wrist multi-axis force/torque sensor and hand camera. The camera. The fingertips were designed to be compliant with the shape of the objects, as it fingertips were designed to be compliant with the shape of the objects, as it is covered by is covered by elastic materials. The robot arm is attached to the body with a three-stage elastic materials. The robot arm is attached to the body with a three-stage prismatic waist prismatic waist joint, which enables the height of the robot’s shoulder to a telescope. The joint, which enables the height of the robot’s shoulder to a telescope. The robot’s head has robot’sbeen attached head has by been an ASUS attached Xtion by RGBD an ASUS camera, Xtion two RGBD RGB camera, cameras, two a microphone RGB cameras, array a microphoneand a small displayarray and monitor a small [39 display–41]. monitor [39–41]. TheThe robot robot has has two two onboard onboard computers. computers. The The main main computer computer is is preserved preserved for for Toyota’s Toyota’s proprietaryproprietary code code to to execute execute sensor sensor and and motor motor interfaces, interfaces, motion motion planning planning and and control control and and thethe web web-based-based remote remote user user interface interface etc. etc. Nevertheless, Nevertheless, both both computers computers do do not not have have much much computingcomputing power power for for the the development development of of advanced advanced software software algorithms. algorithms. Then, Then, it it requires requires anan external external computer that can directlydirectly communicatecommunicate with with the the HSR HSR software software platform platform using us- ingRobot Robot Operating Operating System System (ROS). (ROS). The The robot robot software software architecture architecture can can be categorized be categorized into intotwo two layers: layers: (1) real-time (1) real- controltime control and (2) and intelligence (2) intelligence layers. Thelayers. first The layer first executes layer executes real-time realprocessing-time processing for motor for control motor using control motor using drivers motor and drivers servo and amplifiers. servo amplifiers. The second The layer sec- onddeals layer with deals analysing with analysing or systematically or systematically extracting extracting big data such big data as RGB such images as RGB and images point andclouds, point in clouds, which in Linux which (Ubuntu) Linux (Ubuntu) is adopted is adopted to run theto run OS the and OS ROS and [ROS39–41 [39].– The41]. HRSThe HRSsoftware software architecture architecture is illustrated is illustrated in Figure in Figure1b. 1b.

Microphone array RGBD camera Display monitor HSR External PC Wide-angle camera ROS 6-axis Force/torque sensor ROS GPU: Graphical ROS nodes Hand wide-angle camera Processing Unit Diagnostics Navigation HRH behavioural Stereo camera control strategy Vacuum pad Manipulati Routing algorithm Controller Gripper on TCP/IP protocol Robotic hybrid PID Power button position and Linux 4DOF Manipulator arm Impedance control Real-time Device drivers Controller and Amplifiers Safety algorithms

Lift joint Firmware IMU sensor Sensors Servo motor 1, 2 , Laser range sensor (Force / camera Sensor / ... Magnetic sensor ..) Bumper sensor (a) (b)

FigureFigure 1. 1. ((aa)) The The hardware hardware equipment equipment of of the the HRS HRS and and ( (bb)) HRS HRS software software architecture architecture (connecting (connecting to an an external PC).

Appl.Appl. Sci. Sci.2021 20,2111, ,11 4437, x FOR PEER REVIEW 5 5of of 20 19

2.3.2.3. The The Conceptual Conceptual Framework Framework forfor Human-RobotHuman-Robot HandoverHandover TheThe conceptual conceptual outlineoutline for HRH HRH presents presents in in Figure Figure 2.2 .Based Based on on the the human human-like-like be- behaviouralhavioural guideline, guideline, it itcan can be be summarized summarized that that the the robot robot initi initiallyally takes takes an order an order given given by bya receiver; a receiver; for for example, example, grasping grasping a bottle a bottle of water, of water, etc. Then, etc. Then, the robot the robotmoves moves to the object to the objectlocation location and extracts and extracts the desired the desired object object (i.e., (i.e.,using using YOLO). YOLO). The computational The computational system system cre- createsates a agrasping grasping point point of of the the object object based based on on the the centre centre of of gravity. gravity. Afterwards, Afterwards, the the robot robot plansplans the the hybridhybrid base/arm trajectory trajectory using using the the hybrid hybrid inverse inverse kinematics kinematics explained explained in Sec- in Sectiontion 2.4. 2.4 Once. Once the therobot robot gripper gripper reaches reaches the object, the object, it starts it startsgrabbing grabbing it with it a withproper a propergrasp- graspinging force force measured measured by force by force sensors sensors attached attached to the to robot the robot fingertips. fingertips. The robot The robotmoves moves back backto the to thereceiver receiver and andsimultaneously simultaneously recognize recognize the thereceiver receiver and and his/her his/her location location using using the thehead_pose_estimation head_pose_estimation package package detailed detailed in Section in Section 2.5. 2.5 Subsequently,. Subsequently, the therobot robot moves moves to toa atransfer transfer locat locationion computed computed using using Equation Equation (1). (1).The Thetransferring transferring phase phase is activated is activated once oncethe receiver the receiver manipulates manipulates the hand the hand and andgrasps grasps the object. the object. In the In meantime, the meantime, the service the service ro- robotbot has has to to regulate regulate the the bilateral bilateral force force before before releasing releasing the the object object to to be be transferred transferred to to the the receiver.receiver. This This computational computational process process incorporates incorporates thethe requiredrequired maximum interactive force force formulatedformulated in in Equation Equation (2). (2). Then, Then, the the final final phase phase involves involves the the receiver receiver taking taking responsibility responsibil- fority the for object the object and and the robotthe robot retrieving retrieving the armthe arm and and returning returning to the to the home home position. position. ThisThis paper paper focuses focuses on on the the designdesign andand developmentdevelopment of the robot human human-like-like dynamic control,control, particularly particularly in in the the transferring transferring session. session. This This will will enable enable similarly similarly fluid fluid and and effective effec- objecttive object handover handover tasks tasks with the with receiver. the receiver. Therefore, Therefore, for all for HRH all HRH tests, te itsts, can it be can assumed be as- thatsumed the robotthat the correctly robot correctly holds the holds required the required object and object stays and at a stays home at position a home already.position Ital- is readyready. to It promptly is ready to handling promptly the handling object to the a humanobject to at a ahuman defined at a transfer defined location. transfer location.

Robot Giver Receiver

Take orders given Take orders given by Human by Human Extracting depth information (YOLO) and Generate Detect the object grasping point based on Centre of gravity Move base (Motion planning based on PID+SLAM) Move to the object and Plan arm trajectory (Inverse kinematics)

Reach and grasp object Grasp the object Wait Recognize receiver Detect and track a (Human detection) receiver

Generate transfer location Estimate point E(1) Sending phase Sending

Move to area Move base (Motion planning based on PID) and Plan arm trajectory (Inverse kinematics) Move to point Approach to the Look at the Approach to the point point location

Move to point Wait Wait and grasp

Negotiation using hybrid PID/Impedance Negotiation by control, Plan base and arm trajectory Start pulling it Giver E(2)(3) (Inverse kinematics)

Take Transferphase Open gripper Release hand responsibility

Retrieve the arm and Wait Take back

return to its initial (home) position (and return) Receivingphase

Figure 2. Robot handover framework based on the human–human handover. Appl. Sci. 2021, 11, 4437 6 of 19

2.4. Kinematic Model of the HSR This section briefly explains the kinematic model of the HSR, which relates to forward and inverse kinematics. In the case of the forward kinematics, the location and pose of the robot end-effector are controlled by the given links and joints of the HSR robot. The Denavit–Hartenberg parameters evaluation is used to achieve the kinematic chain of the robot rigid bodies. Based on the studies by researchers [39–41], the mechanical schematic diagram of a dual-wheel caster-drive robot base and the robot’s joint configuration is shown in Figure3a. The following notation is used in this analysis: the radius of each wheel (r), the angle of the pivot axis (θH), the wheel tread (W), the displacement between the centre axis and the axle centre (H), the inverse kinematics ( f ), the reference value of the tip pose (Te), the degree of freedom of the base (θ2), the torso lift height (l3), the shoulder pitch angle (θ4), the length of arm (l52), the length of the wrist(l82) and finally, the joint angles of the arm (θ5, θ6, θ7). These are defined in Figure3b. The robotic dynamic model is essential for describing the HSR robot’s position, ori- entation and motions of the wheels. It can be used for analysing and synthesizing the overall dynamic behaviour of the robot. The robot itself has three degrees of freedom on the x-y plane and can perform forward–reverse movement and left–right movements. As reviewed [39–41], the kinematic equations and relations between robot velocity and various geometry specifications are expressed by the Transformation matrices from linear velocity on the global frame to linear on the robot frame as the following.

−1 .   ω   r cosθ − rH sinθ r cosθ + rH sinθ 0  x R 2 H W H 2 H W H . ω = r sin − rH cos r sin − rH cos  y . (4)  L   2 θH W θH 2 θH W θH 0   .  r r ωH W W 1 θ

By assuming that translational and rotational transforms on x-y-z axes are represented as Tt(x, y, z), Rx, Ry, Rz and Rz, respectively, forward kinematics of the HSR arm (THSR) can be written as:

THSR = Rz(θ2)Te(0, 0, l3)Ry(θ4)Te(0, 0, l52)Rz(θ5)Ry(θ6)Rz(θ7)Te(0, 0, l82). (5)

According to the kinematic of the HSR arm, the base yaw angle (θ2) can be calculated using the relative wrist position (x, y and z) as:

T T [x y z 1] = f Te(0, 0, −l82)[0 0 0 1] (6)

θ2 = atan2(y, x) (7)

Analysing torso lift height (l3) and shoulder pitch angle (θ4) gives:  q   q  − 2 − 2 − 2 − 2 − 2 − 2 l3 z l52 x y l3 z l52 x y =   or =  . (8) θ p 2 2 θ p 2 2 4 arcsin x + y /l52 4 π − arcsin x + y /l52

Investigating the joint angles of the arm (θ5, θ6, θ7) based on the matrix TR offers:

TR = Te(0, 0, −l52)Ry(−θ4)Te(0, 0, −l3)Rz(−θ2) f Te(0, 0, −l82), (9)     θ5 atan2(R23, R13)  θ6  =  arccos(R33)  (10) θ7 atan2(R32, −R31) However, to allow the HSR to interact naturally with a human and to facilitate the dexterous transfer of objects promptly, hybrid inverse kinematics proposed by Yamamoto et al. [41] was implemented. It involves the inverse kinematics for the eight-DOF HSR including the robot base with three DOF and the arm with ﬁve DOF. The hybrid inverse Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 20 Appl. Sci. 2021, 11, 4437 7 of 19

kinematics (푓) for the HSR can be formulated using the robot tip pose (푇푒) as in the fol- kinematics ( f ) for the HSR can be formulated using the robot tip pose (Te) as in the following lowing equation: equation: = (θ(0휃,0θ,1휃,1θ,3휃,3θ,5휃,5θ,6휃, 6θ,7휃)7) =f (푓T(e푇,푒θ,2휃,2θ,4휃)4) (11)(11) re f 푟푒푓 Given θ휃==((θ휃00,,θ휃11,,θ휃22,,θ휃33,,θ휃55, ,θ휃66, θ, 휃7)7)and and the the desired desired reference reference joint joint angles angles (θ (휃) of )θ ofand 휃 andthen then the evaluationthe evaluation function function (V) can(푉) becan identiﬁed be identified as: as: 푟푒푓 푟푒푓 푉(휃2, 휃4, 푇푒, 푊,re휃 f ) = ‖푊(휃re f − 휃)‖ (12) V θ2, θ4, Te, W, θ = W θ − θ || (12) The evaluation function (푉) can be minimized using 휃2 and 휃4 and expressed as: The evaluation function (V) can be minimized using θ2 and θ4 and expressed as: arg 푉 (휃 , 휃 , 푇 , 푊, 휃푟푒푓). 푚푖푛 2 4 푒 (13) re f argV θ , θ , Te, W, θ . min 2 휃42, 휃4 (13) θ2, θ4

x q4 qH,wH

z q3 l3 y Pivot joint y Hz Hinge joint r q2 q1 Translation joint qH,wH W q0 x (a) (b) Figure 3. (a) The dual-wheel caster-drive robot base and (b) the robot join configuration. Figure 3. (a) The dual-wheel caster-drive robot base and (b) the robot join conﬁguration.

2.5. Coordinate Registration in the HRH System This section proposesproposes allall coordinate coordinate registration registration for for the the HRH HRH system. system. It consists It consists of the of therobot robot base base frame frame (robot (robot reference reference frame) frame) [R], the[R], robot the robot end-effector end-effector frame frame [A], object [A], object (held (heldby the by robot) the robot) frame [T],frame RGBD [T], RGBD camera camera frame [C],frame human [C], human frame [H] frame and [H] handover and handover location locationframe [L]. frame All relative[L]. All relative frames inframes the HRH in the system HRH system are drawn are indrawn Figure in 4Figure. It is to 4. beIt is noted to be notedthat all that the all reference the reference frames frames are in Cartesianare in Cartesian coordinates. coordinates. According According to the humanto the human frame frame[H] can [H] be can located be located by a tracking by a tracking control control based based on the on head_pose_estimation the head_pose_estimation package package [42] [42]as mentioned as mentioned previously. previously. A human A human head head is identiﬁed is identified based based on a on personalized a personalized template tem- plateby the by spherical the spherical kernel kernel using using a supervised a supervised learning learning algorithm, algorithm, namely namely a discriminative a discrimina- tiverandom random regression regression forests forests (DRRF) (DRRF) technique. technique. The human The human nosetip nose vector tip vector is a crucial is a crucial aspect aspectthat can that describe can describe the changes the changes of the headof the pose head neatly. pose neatly. Subsequently, Subsequently, the nose the tip nose frame tip frameis appropriately is appropriately used asused the as human the human reference reference frame frame [H]. Then,[H]. Then, it can it becan noted be noted that that the thehandover handover location location frame frame [L] is[L] associated is associated with with the humanthe human (nose (nose tip) tip frame) frame [H]. [H]. The frame relationship between the object held by the robot and the human hand is the chief requirementrequirement implemented to successfully control the robot movements in the HRH tasks. TheThe TransformationTransformation matrix matrix has has been been investigated investigated to to identify identify the the posture posture of the of theobject object [T] held[T] held by theby the HSR HSR end-effector end-effector [A] relative[A] relative to the to robotthe robot reference reference frame frame [R]. [R]. It can It canbe implied be implied that that the robotthe robot should should manipulate manipulate the held the objectheld object to the to receiver the receiver naturally naturally at the athandover the handover location location (incorporating (incorporating Equation Equation (1)). The ( Transformation1)). The Transformation matrix of matrix the handover of the handoverlocation frame location [L] relativeframe [L] to relative the robot to basethe robot frame base [R] canframe be [R] determined can be determined based on (1)based the Appl. Sci. 2021, 11, 4437 8 of 19

[L] referenced by the [C] and (2) the [L] referenced via [R]. Hence, their relationship can be written as Equation (14). R R A R C H L T T = AT·T T = C T·H T·L T·T T (14) i where jT denotes the coordinate transformation from the j frame relative to the i frame. The R C T T can be solved using the robot forward kinematics equations. The H T can be measured R H by the tracking system. The C T can be adopted from the robot conﬁguration and L T can L be calculated using Equation (1) conducted by the HHH experiments. Finally, the T T can be computed using the following equation:

−1 −1 −1 L H C R R A T T = L T · H T · C T ·AT·T T (15)

The transformation matrix of the [L] frame relative to the [R] can be expressed by Appl. Sci. 2021, 11, x FOR PEER REVIEW 8 of 20 Equation (16). −1 R R A L L T = AT·T T· T T (16) on (1)The the relationship [L] referenced is subsequentlyby the [C] and implemented (2) the [L] referenced on the real-time via [R]. Hence, hybrid t PIDheir positionrelation- andship impedancecan be written control as Equation of the robot (14) compliant. motions in HRH.

[H]

[C] [T] [L]

[A]

Y [R] X

FigureFigure 4.4. Reference framesframes ofof thethe HRHHRH system.system.

2.6. Robotic Hybrid Position/Force푅 Control푅 Strategy퐴 푅 for퐶 the HRH퐻 퐿 Tasks 푇푇 = 퐴푇 ∙ 푇푇 = 퐶푇 ∙ 퐻푇 ∙ 퐿푇 ∙ 푇푇 (14) Robot force control is a fundamental requirement in the achievement of the control where 푖푇 denotes the coordinate transformation from the 푗 frame relative to the 푖 of the robot’s푗 real-time path in any physical HRI task. It has been developed in the past frame. The 푅푇 can be solved using the robot forward kinematics equations. The 퐶푇 can three decades,푇 using for example force, torque and visual feedback to operate robots퐻 to 푅푇 suitablybe measured participate by the tracking in unstructured system. The environments. 퐶 can be adopted Raibert andfrom Craig the robot [43] configuration introduced a 퐻푇 newand method퐿 can befor calculated robot force using control Equation based on (1) two conducted control loops, by theconsisting HHH experiments. of combined Fi- 퐿푇 positionnally, the and 푇 forcecan be control computed systems, using namely the following hybrid equation position/force: control. One of the crucial advantages of using the퐿 hybrid퐻 − control1 퐶 is−1 that푅 the−1 position푅 퐴 and force information is 푇푇 = ( 퐿푇) ∙ (퐻푇) ∙ (퐶푇) ∙ 퐴푇 ∙ 푇푇 (15) analysed synchronously and independently. These two outputs are combined in the ﬁnal stageThe before transformation being converted matrix to drive of the the [L] robot frame actuators relative using to the joint [R] torquecan be controlexpressed [44]. by EquationIn this (16). research, hybrid PID position/impedance control was implemented. This is due to the fact that PID position control푅 푅 is the퐴 most퐿 − widely1 used and reliable method 퐿푇 = 퐴푇 ∙ 푇푇 ∙ (푇푇) (16) for controlling a robot manipulator position. Additionally, a method for the automatic tuningThe of relationship PID controllers is subsequently in a closed loop implemented can be estimated on the using real- atime transfer hybrid function PID position model. However,and impedance the PID control controller of the may robot not compliant suitable for motions force control in HRH. because the derivative term (Kd) is too sensitive to noise. This could induce a destabilizing impact on the robot force control2.6. Robotic system Hybrid [45]. Position/Force Interestingly, Control Rahman Strategy et al. [ 46for] t proposedhe HRH Tasks the mathematical models of the humanRobot handforce while control interacting is a fundamental with a HHH requirement task. The humanin the achievement applied force of is formulatedthe control byof the the robot’s human real arm-time impedance path in whichany physical is made HRI up task. of mass, It has stiffness been developed and damping in the factor, past three decades, using for example force, torque and visual feedback to operate robots to suitably participate in unstructured environments. Raibert and Craig [43] introduced a new method for robot force control based on two control loops, consisting of combined position and force control systems, namely hybrid position/force control. One of the crucial advantages of using the hybrid control is that the position and force information is analysed synchronously and independently. These two outputs are combined in the final stage before being converted to drive the robot actuators using joint torque control [44]. In this research, hybrid PID position/impedance control was implemented. This is due to the fact that PID position control is the most widely used and reliable method for controlling a robot manipulator position. Additionally, a method for the automatic tuning of PID controllers in a closed loop can be estimated using a transfer function model. How- ever, the PID controller may not suitable for force control because the derivative term (퐾푑) is too sensitive to noise. This could induce a destabilizing impact on the robot force control system [45]. Interestingly, Rahman et al. [46] proposed the mathematical models of the human hand while interacting with a HHH task. The human applied force is formulated by the human arm impedance which is made up of mass, stiffness and damping factor, Appl.Appl. Sci. Sci.2021 2021, 11, 11, 4437, x FOR PEER REVIEW 9 ofof 1920

respectively.respectively. Consequently,Consequently, to to imitate imitate the the real-world real-world human human force force dynamics, dynamics, impedance impedance control,control, whichwhich efﬁcientlyefficiently controls controls both both the the motionmotion of of the the robot robot and and its its contact contact forces,forces, was was selectedselected inin thethe roboticrobotic force force control control loop. loop. FigureFigure5 5illustrates illustrates the the hybrid hybrid PID PID position/impedance position/impedance control control scheme, scheme, which which was was designeddesigned basedbased on the the study study by by Vukobratovic Vukobratovic et etal. al. [47]. [47 It]. is It made is made up of up two of control two control loops loopseach of each which of has which an individual has an individual information information sensor system, sensor i.e. system,, (1) multi i.e.,-axis (1) force/torque multi-axis force/torquesensor used to sensor detect used the toforce detect applied the force to the applied HSR robot to the end HSR effector robot and end (2) effector the rotary and (2)encoders the rotary used encoders to measure used the to robot measure joint thepositions. robot joint푋푅 and positions. 퐹푅 representXR and theF referencesR represent of thethe referencesposition and of theforce position control and schemes, force controlwhich can schemes, be generated which canusing be handover generated experi- using handovermental results, experimental as explained results, in Equations as explained (1) inand Equations (2). The outcomes (1) and (2). from The the outcomes position from (푋푃) theand position force control (XP) and(푋퐹) forceloops control are fused (X Ftogether) loops areas a fused set of together incremental as a displacements set of incremental (푋푢), displacementsnamely, the hybrid (Xu), namely, position/force the hybrid control position/force, as exhibited control, in Equation as exhibited (17). inThe Equation parameters (17). The푋퐸 and parameters 퐹퐸 are XtheE anderrorFsE ofare the the position errors of in the Cartesian position coordinate in Cartesian and coordinate force control and forcealgo- controlrithms, algorithms, respectively. respectively. The matrix The 푆 represents matrix S represents the compliance the compliance selection selection matrix used matrix to usedspecify to specifythe number the number of DOF of in DOF the inrobotic the robotic control control system, system, where where 퐴 andA and퐵 representB represent the therobotic robotic position position and and force force control controllers,lers, respectively. respectively. 푋 = 푋 + 푋 = 퐴[(푆 × 푋 )] + 퐵[((1 − 푆) × 퐹 )] Xu =푢XP +푃 XF =퐹 A[(S × XE퐸)] + B[((1 − S) × Fe)]푒 (17)(17)

DRGB camera

Coordinate transformation

HRH

X +

_ XE XP XU qu qs X + PID Position + HSR Forward S J-1 control rule controller kinematics

XF XR Impedance force Encoders Trajectory planner X control rule with (predefined position) double integral

1-S

FR + FS Required _ Force/Torque interactive force sensor

FigureFigure 5. 5.Hybrid Hybrid PID PID position/impedance position/impedance forceforce controlcontrol designeddesigned forfor thethe HSRHSR inin thethe HRHHRH tasks.tasks.

TheThe positionspositions ofof thethe robotrobot inin jointjoint spacespace ((q푞u푢)) can can be be computed computed based based on on an an inverse inverse JacobianJacobian matrixmatrix as as expressed expressed in in the the following following equation. equation. −1 푞푢 = 퐽(−푞1) 푋푢 (18) qu = J(q) Xu (18) The position feedback of the robot in Cartesian space (푋) is adopted the forward kin- ematicThe function position based feedback on the of position the robot feedback in Cartesian of the space robot ( Xjoint) is adoptedspace detected the forward by the kinematicencoders ( function푞푠) as formulated based on thein Equation position feedback(19). of the robot joint space detected by the encoders (qs) as formulated in Equation (19). 푋 = 푓(푞푠) (19) = ( ) According to the robot control design,X a fPIDqs controller was implemented on the robot(19) position control. An incremental discrete-time PID control algorithm with a sampling pe- According to the robot control design, a PID controller was implemented on the robot riod (휏) and the discrete-time interval 푘 was implemented. The robotic position control position control. An incremental discrete-time PID control algorithm with a sampling periodscheme(τ is) andwritten the as: discrete-time interval k was implemented. The robotic position control

푋푃(푘) = 푋푃(푘 − 1)scheme+ (푆)[푘 is푝( written푋퐸(푘) − as:푋퐸(푘 − 1)) + 푘푖(푋퐸(푘)) + 푘푑(푋퐸(푘) − 2푋퐸(푘 − 1) + 푋퐸(푘 − 2))] (20) XP(k) = XP(k − 1) + (S) kAdditionally,p(XE(k) − XE the(k − robot1)) + haski( XtoE (interactk)) + k dwith(XE( ak )human− 2XE (ink − HRH,1) + inXE which(k − 2 ))the muscles(20) of the human arm can be mechanically represented in a mass-spring and damper system Appl. Sci. 2021, 11, 4437 10 of 19

Additionally, the robot has to interact with a human in HRH, in which the muscles of the human arm can be mechanically represented in a mass-spring and damper system [46]. To facilitate the robotic impedance behaviour, the impedance control approach is therefore executed, which is not only used to track a motion trajectory alone but rather to regulate the mechanical impedance (Zm) of the robot. The impedance control can be exposed as: .. .. . . FE = Md XF − X + Bd XF − X + Kd(XF − X) (21)

The real-time trajectory of the robot end-effector can be controlled by acceleration as the following equation; however, the acceleration output incorporates a double integral function before being transmitted to the robot.

...... −1h i XF = X + Md FE − Bd X − XF − Kd(X − XF) (22)

where Md, Bd and Kd represent a designed inertia matrix, damping matrix and stiffness matrix, respectively, which incorporate with FE.

2.7. Dynamic Mechanical Model of the Physical Human-Robot Handover This section summarized a mathematical dynamic model of the HRH system. Presum- ably, the overall interaction can be represented as sub-equivalent-lumped-mass systems as depicted in Figure6. The HSR can be modelled by the rigid body model ( mR) associated with effective viscous damping (bR) to ground and driven by force FR created by the hybrid control algorithm. The robot hand (gripper), which holds the rigid object mass mO, has the impedance parameters as mh, kh and bh, respectively. The force sensor attached to the hand wrist can be mechanically presented as a spring (ks) and dashpot (bs) system. The ∗ impedance parameter notation of the human hand comprises the hand mass (mH), the viscous damping (bH) and stiffness (kH), respectively. In addition, the displacements of the masses (mR, mh and mH) are, respectively, deﬁned as xR, xh and xH, in which it can ∗ be assumed as: mH=mO+mH. The state-space equations based modelling representing the overall HRH system can be formulated as:     0 1 0 0 0 0   xR ( + ) xR .  − ks − bR bs ks bs 0 0  .  xR   mR mR mR mR  xR        xh   0 0 0 1 0 0  xh  d . = . dt    ks bs (kh+ks) (bh+bs) kh bh    xh   − −  xh     mh mh mh mh mh mh    xH    xH  .  0 0 0 0 0 1  . ( + ) ( + ) xH 0 0 kh bh − kh kH − bh bH xH mH mH mH mH (23)      0 0 0 0 0 0 F xR .  1 0 0 0 0 0  0 x  mR     R     1 0 0 0 0 0    0 0 0 0 0 0  0   xh  +   and y =  0 0 1 0 0 0  .  + [0]F  0 0 0 0 0 0  0   xh     0 0 0 0 1 0   0 0 0 0 0 0  0   xH    . 0 0 0 0 0 0 0 xH After taking the Laplace transforms of the state space equations above, the transfer matrices are then given by:

xR(S) A xh(S) B xH(S) C G1(s) = = , G2(s) = = , G3(s) = = (24) FR(S) D FR(S) D FR(S) D

where Appl. Sci. 2021, 11, x FOR PEER REVIEW 10 of 20

[46]. To facilitate the robotic impedance behaviour, the impedance control approach is therefore executed, which is not only used to track a motion trajectory alone but rather to regulate the mechanical impedance (푍푚) of the robot. The impedance control can be exposed as:

퐹퐸 = 푀푑(푋̈퐹 − 푋̈) + 퐵푑(푋̇퐹 − 푋̇) + 퐾푑(푋퐹 − 푋) (21) The real-time trajectory of the robot end-effector can be controlled by acceleration as the following equation; however, the acceleration output incorporates a double integral function before being transmitted to the robot. −1 푋̈퐹 = 푋̈ + 푀푑 [퐹퐸 − 퐵푑(푋̇ − 푋̇퐹) − 퐾푑(푋 − 푋퐹)] (22)

where 푀푑, 퐵푑 and 퐾푑 represent a designed inertia matrix, damping matrix and stiffness matrix, respectively, which incorporate with 퐹퐸.

2.7. Dynamic Mechanical Model of the Physical Human-Robot Handover This section summarized a mathematical dynamic model of the HRH system. Pre- sumably, the overall interaction can be represented as sub-equivalent-lumped-mass systems as depicted in Figure 6. The HSR can be modelled by the rigid body model (푚푅) associated with effective viscous damping (푏푅) to ground and driven by force 퐹푅 created by the hybrid control algorithm. The robot hand (gripper), which holds the rigid object mass 푚푂, has the impedance parameters as 푚ℎ, 푘ℎ and 푏ℎ, respectively. The force sensor attached to the hand wrist can be mechanically presented as a spring (푘푠) and dashpot (푏푠) system. The impedance parameter notation of the human hand comprises the hand mass ∗ (푚퐻), the viscous damping (푏퐻) and stiffness (푘퐻), respectively. In addition, the displacements of the masses (푚푅, 푚ℎ and 푚퐻) are, respectively, defined as 푥푅, 푥ℎ and 푥퐻, in ∗ Appl. Sci. 2021, 11, 4437 which it can be assumed as: 푚퐻=푚푂+푚퐻. The state-space equations based modelling11 ofrep- 19 resenting the overall HRH system can be formulated as:  0 1 0 0 0 0  x   k (b  b ) k b x  R  s  R s s s 0 0 R    A = (khks + khkH + kskH + bhksS + bskhS + bhkHS + bHkhS +bskHS + bHksS+ xR mR mR 2 mR2 m2 R 3 3 3 xR 3 3    bhbsS + bhbHS + bsbHS + bhmhS + bhmhS + bhmHS+ bHmhS + bsmHS +    0 0 0 1 0 0   d xh k m S2 + k m S2 + k m S2 + k m S2 + m m S4) xh   ks bh s h (hkh H ks ) H(bhh bs ) s H kh h H bh      2   dt xh B = ((ks + Sbs)(kh + kH + bhS + bHS + mHS ) xh    mh mh mh mh mh mh        xH 0C = ((0kh + Sbh)(ks0+ Sbs)) 0 0 1 xH    3 3 3 3  2 D = (khkskH + bhbkRh bsS + bhbbRhbHS + b(khbh sbkHHS) + b(RbbhsbHbHS) + bhbRksS + xH   0 0   xH    2 2 2 2 2  2  bRbskhS + bmhbHRkHS + bmRHbHkhS + bmhbHskHS + bhmbHksS + bsbHkhS + 2 2 4 4 4 4 (23) bRbskHS + bRbHksS + bhbRmhS + bhbsmhS + bhbsmRS + bhbRmHS + x  0 0 0 b0b 0m 0S4 + b b m S4 + b b m S4 + b b mR  S4 + b b m S4 + b b m S4+ h H R F R H h h s Hw s H h R s H s H R  1 3  3 3 x 3 3 3 0 0 bR0kh0mh0S +0 bhksmhS + bskhmhS + bhksmRSR + bskhmRS + bhkHmRS + m   1 0 0 0 0 0 R 3 and 3 3  3 3 3  bRkhmHS 0+ bRkHmhS + bwkgmrS + bgksmxwh S + bskgmwS + bskwmgS +  0 0 0 0 0 0 y  0 0 1 0 0 0    0F  b k m S3+b k m S3 + b k m S3 + b k mx S3 + b m m S5 + b m m S5+  0 0 0 w0 s0 g0 0 r s w s H R H s  Rh  h h R h R H 5  50 0 0 0 15 0 5 2 2  bRmhmHS + bHmhmRS + bsmhmHS + bsmxRm HS + khksmhS + khksmRS + 0 0 0 0 0 0  0  H  k k m S2 + k k m S2 + k k m S2 + k k m S2 + k m m S4 + k m m S4+  h H R  0 h s H s H h s Hx R h h R h R H  0 0 0 0 0 04  4 4  H  6 kHmhmRS + ksmhmHS + ksmRmHS + mhmRmHS + bRkhksS + bRkhkHS+ bhkskHS + bskhkHS + bHkhksS + bRkskHS

xR xh bR ks kh kH

* mR mh mO MH F R bs bh bH Force Robot Human Sensor Object Hybrid control

Figure 6. Mechanical schematic of the HRH based on a second-order-lumped-mass model.

2.8. Safety in HHH One of the principal challenges in the development of the HRH task is a safety issue since any failure might become very critical for a human co-worker. Several studies have investigated relative approaches to safety and reliability issues in HRI, i.e., human factors, risk assessment, hazard analysis and technologies for HRI safety, etc. Matthias et al. [48] presented an assessment of the associated risks for possible incidental contact events between a collaborative robot and a human worker based on the guidance published in ISO/TS 15066. The ISO/TS 15066 provides standards of safety and operation for the complex protection schemes required for robots. Many researchers [49–51] proposed conceptual designs to improve safety issues in HRI, such as applying appropriate emergency stops which can be activated as soon as any error occurs, or implementing a validation phase based on control software to facilitate an effective real-time control system etc. In the robotic system, a safeguarded zone was introduced to conduct a safety strategy that can prevent unauthorised humans from entering the test area. The speed of the HSR movement allowed in the HRI tests was limited and the robot working area was also optimized to minimize the risk of an injury. A timeout, which indicates the period of time allowed for executing a speciﬁed task, was assigned for the serial and TCP/IP communication. Safety upper-limit force (flim) was deﬁned. Based on the HHH study [32], the flim was recommended to initially set at 10 N indicating the amount of magnitude of maximum force is taken into account when the handler decides to release the object to be transferred naturally. If the exerted (interactive) force is greater than the limited force, the robot will be immediately terminated. Moreover, the stand-alone emergency stop buttons can be manually activated by a human operator when accidents are detected.

3. Results and Discussion The HHH cooperation has provided a better understanding of what is required in the development of an appropriate set of human-like behaviour for the robot control strategy. Appl. Sci. 2021, 11, 4437 12 of 19

This can facilitate the dextrous HRH tasks in a safe and effective way. In this research, hybrid PID position and impedance control has been developed and implemented to improve the stability of the robot position and force control. Nevertheless, the key challenge is to suitably design the optimized PID and impedance control algorithms of the HSR, which interacts with the complicated dynamic nature of the human in the object handovers. Subsequently, this section details the evaluation of the performance of the robot control system to facilitate the robot to transfer the object to the human receiver in a safe, timely and natural manner.

3.1. Optimization of the Robotic PID Position Control

To successfully adopt an appropriate PID controller, the control gains (kp, ki and kd) can be estimated using the Ziegler–Nichols tuning method incorporated with the robot transfer function of the proposed dynamic model in Section 2.7. However, it is complicated to estimate each model parameter in the dynamic equations and then the MATLAB system identiﬁcation was consequently applied. One of the effective parametric models identifying system transfer functions which are recommended by Ruslan et al. [52] is the Autoregressive Moving Average eXogenous (ARMAX). The ARMAX is one of the most robust techniques and is normally used to study complex dynamical systems in time-series analysis. The Z–transform is reasonable for analysis of practical model identiﬁcation, which presents the relationship between the eigenvalues and the poles of the ARMAX-based estimated model. The model [53] can be formulated as follows:

y(t) + a y(t − 1) + ··· + a y(t − n ) = b u(t − 1) + ··· + b u(t − n ) + e(t) 1 na a 1 nb b (25) +c1e(t − 1) + ··· + cnc e(t − nc)

where the observed input and output are represented as u(k) and y(k). The noise signal moderated into the system is e(k). The Matlab Identification Toolbox was implemented to appropriately estimate the model unknown ARMAX parameters by selecting a set of a polynomial of na, nb and nc to minimize the forecasting errors and to obtain an effective model. A set of preliminary experiments involving a robot position control system has been conducted. The time-series information required in the system identification is made up of (1) robot position target, namely the ARMAX input and (2) the measured robot position, namely the ARMAX output. Presumably, the range of the robot forward translation movement is approximately 1 meter. Additionally, to present the system-parameter estimation technique under consideration adequately, the model validation finally verifies a proposed identified model. The discrete- time ARMAX model was identified and its results are given in Equation (26). The set of minimized polynomial functions is made up of na : 2, nb : 2 and nc : 2, respectively.

−1 −na −1 −2 A(z) = 1 + a1z + ··· + ana z = 1 − 1.993 z − 0.9934z −1 −nb −1 −2 B(z) = b0 + b1z + ··· + bnb z = 0.0001068z − 9.555e − 05z (26) −1 −nc −1 −2 C(z) = 1 + c1z + ... + ana z = 1 − 0.9241z − 0.02802z

It is to be noted that the MATLAB discrete-time to continuous-time model conversion was then applied to determine the robotic transfer function into a continuous-time transfer function (HSRTF) as formulated in Equation (27).

1.036e − 05s2 + 0.002733s + 0.03113 HSR = (27) TF s2 + 1.666s + 0.7003 Model validation was carried out in the final stage of the model identification process to verify the simulated model. Several model validation techniques were then employed and the results express as follows: (1) percentage of best fit (R2) was approximately 100%, (2) mean squared error (MSE) was 1.069 × 10−10 and finally, (3) final prediction error (FPE) was 1.077 × 10−10, respectively. Using the predicted model indicates that the actual and Appl. Sci. 20212021, 11, 4437x FOR PEER REVIEW 1313 of of 1920

estimated data have high similarity in terms of the system responses. Then, it is consid- estimated data have high similarity in terms of the system responses. Then, it is considered eredacceptable acceptable for the for HSR the HSR representation representation model. model. After executing ZieglerZiegler Nicholas Nicholas PID PID tuning tuning in in Matlab Matlab by by providing providing the numeratorthe numerator and anddenominator denominator coefficients coefficients of the of HSR the HSR mathematical mathematical model model in the in Laplace the Laplace domain, domain, it gives it gives gain parameters the critical gain 푘 and ultimate period 휏 . Consequently, it can gain parameters the critical gain kc and ultimate푐 period τc. Consequently,푐 it can compute compute the gain values of 푘푝, 푘푖 and 푘푑. Nevertheless, the tuning technique still needs the gain values of kp, ki and kd. Nevertheless, the tuning technique still needs an operator’s anexperience operator’s for experience fine-tuning for until fine the-tuning system until specification the system is achieved.specification This is offers achieved. optimized This offers optimized PID gains as 푘 , 푘 and 푘 of 0.24, 0.13 and 0.02, respectively. To eval- PID gains as kp, ki and kd of 0.24,푝 0.13푖 and 0.02,푑 respectively. To evaluate the closed-loop uatestability the closed of the- robotloop stability system, of Figure the robot7 illustrates system, the Figure test results 7 illustrates and frequency the test results response and frequencyanalysis, whereas response the analysis, robot moved whereas forwards the robot to the moved 1 m target. forwards It can to bethe summarized 1 m target. thatIt can a beset summarized of the optimized that PIDa set gains of the demonstrates optimized PID significant gains demo satisfactionnstrates withsignificant the overall satisfaction control withperformance the overall in thecontrol robotic performance control system in the and robotic this iscontrol subsequently system and used this inthe is subsequently robot hybrid usedcontrol in approach.the robot hybrid control approach.

Displacement diagram Bode diagram

)

(

)

(

Magnitude

)

Displacement

deg

( Phase Phase

Time (s) Frequency (rad/s) (a) (b)

Figure 7. ((a)) Robot displacement profile proﬁle in the HRH test and (b) Bode magnitude and phasephase plot.plot.

3.2. Evaluation Evaluation of of the the Robotic Impedance Control Based on the HRH This section proposes how to achieve the appropriate impedance force control based on the performance evaluations of the robotic dynamic responseresponse in the human-to-robothuman-to-robot handovers. According According to to the the test procedure, a set of 20 participants has undertaken three repetition sets sets of of each each handover handover test test under under the the required required variable variable conditions. conditions. They They are de- are fineddefined as asa receiver a receiver agent agent and and have have to to grasp grasp the the obj objectect using using one one hand hand and twisting the object is is not not allowed. allowed. The The object object was was fabricated fabricated as asa bottle a bottle-shaped-shaped object object with with 60 mm 60 mm diameter,diameter, 270 270 mm mm length length and and a total a total mass mass of of1.25 1.25 N. N. The The HRH HRH experimental experimental apparatus apparatus is shownis shown in Figure in Figure 8. The8. The HSR HSR is required is required to grasp to grasp an object an object and move and move facing facing to the to object the handoverobject handover location, location, while the while human the human performs performs standstill standstill in comfortable in comfortable positions positions on op- positeon opposite sides of sides the robot. of the robot.Once the Once object the is object manipulated is manipulated to the transfer to the transfer location location the human the ishuman then allowed is then allowed to grab to the grab object the objectand start and naturally start naturally pulling pulling the object the object before before taking taking the ) responsibilitythe responsibility for for the the object object finally. finally. In In the the meantime, meantime, interactive interactive force force ( (퐹F푖푛푡int) profilesprofiles against time (t푡)) presentingpresenting howhow thethe robot robot giver giver regulates regulates the the bilateral bilateral force force before before releasing releas- ingthe the object objec tot be to transferred be transferred to the to receiverthe receiver was was measured measured in real-time. in real-time. The circumstances affecting the robot dynamic behaviour compose of spring stiffness (Kd) and damping factor (Bd), whereas Md was strictly specified as a constant parameter of 0.25 Kg. Initially, a set of preliminary tests was undertaken to establish the relationship between the interactive force and handover time under various spring stiffness (Kd) and damping factor (Bd) values. The goals of the experiments are to understand how the influential factors affect the interactive forces in the HRH system and to determine the ranges of Bd and Kd used in the substantive HRH. The test outcomes were demonstrated in Table1. According to human active comfort, the impedance control system should Appl. Sci. 2021, 11, 4437 14 of 19

have lower stiffness and damping coefficient values. Conflictingly, in terms of active safety, it should have lower stiffness but a higher damping coefficient. Based on the recommendations of the preliminary study, as the humans generally felt comfortable in Appl. Sci. 2021, 11, x FOR PEER REVIEW 14 of 20 the natural physical HRH, Bd should be set as 50 and 75 (NmS/rad) and the range of Kd should be varied from 0 to 400 (Nm/rad), respectively.

Handover location

Move direction

HSR robot controller

Robot giver Human receiver

FigureFigure 8. 8.Experimental Experimental setupsetup ofof thethe robot-to-humanrobot-to-human object object handover handover system. system.

Table 1. Comparisons of the averageThe interactive circumstances force (Fint affecting) and time the (t )robotparameters, dynamic while behaviour individually compose varying of the spring damping stiffness factor (Bd) and spring stiffness(퐾푑 (K) dand), respectively. damping factor (퐵푑), whereas 푀푑 was strictly specified as a constant parameter of 0.25 Kg. Initially, a set of preliminary tests was undertaken to establish the relationship Varied Damping Factor (B ) Varied Spring Stiffness (K ) between thed interactive force and handover time under various springd stiffness (퐾푑) and Bd (NmS/rad) Kd (Nm/rad)dampingF intfactor(N) (퐵푑) tvalues.(s) TheB dgoals(NmS/rad) of the experimentsKd (Nm/rad) are to understandFint (N) howt (s)the in- 25 0 (N/A)fluential factors 1.19 affect 2.14 the interactive 0 (N/A) forces in the HRH 50 system and 0.97 to determine 3.03 the ranges of 퐵 and 퐾 used in the substantive HRH. The test outcomes were demonstrated 50 0 (N/A) 1.16푑 푑 2.22 0 (N/A) 100 1.00 2.99 in Table 1. According to human active comfort, the impedance control system should have 75 0 (N/A)lower stiffness 1.12 and damping 2.32 coefficient 0 (N/A) values. Conflictingly, 150 in terms 1.04 of active safety, 2.92 it 100 0 (N/A)should have 1.09 lower stiffness 2.41 but a higher 0 (N/A) damping coefficient. 200 Based on 1.07 the recommenda- 2.90 125 0 (N/A)tions of the 1.05 preliminary 2.49 study, as 0the (N/A) humans generally 300 felt comfortable 1.16 in the 2.84 natural 퐵 (NmS/rad) 퐾 150 0 (N/A)physical HRH, 1.00 푑 should 2.61 be set as 0 (N/A)50 and 75 400 and the range 1.20 of 푑 should 2.80 be varied from 0 to 400 (Nm/rad), respectively.

Table 1. Comparisons of the averageThe substantive interactive HRHforce ( experiments퐹푖푛푡) and time were (푡) parameters, successfully while carried individually out by the varying 20 selected the damping factor (퐵푑) and springsubjects. stiffness A comparison(퐾푑), respectively of the. average interactive force and handover time under the several conditions based on the HRH tasks is given in Table2. After careful observation Varied Dampingwith Factor regard (푩 to풅) the HRH test results, it wasVaried complicated Spring to Stiffness select the (푲 set풅) of optimized 푩풅 (NmS/rad) 푲풅 (Nm/rad)impedance 푭풊풏풕 parameters (N) 풕 as(s) its nature푩풅 (NmS/rad) necessitates 푲 human풅 (Nm/rad) judgments. 푭풊풏풕 (N) After asking풕 (s) the 25 0 (N/A)participants, 1.19 two desirable2.14 choices0 of (N/A) the impedance parameters50 were0.97 selected based3.03 on 50 0 (N/A)the recommendations,1.16 2.22 which consist0 (N/A) of Set 1: Bd(50),100Kd (200) and 1.00Md( 0.25) and2.99 Set 2: 75 0 (N/A)Bd (75), Kd(501.12) and Md2.32(0.25 ), as highlighted0 (N/A) in Table2150. A questionnaire1.04 technique2.92 was 100 0 (N/A)subsequently 1.09 employed 2.41 to gather information0 (N/A) from the200 human participants.1.07 Rating2.90 scale 125 0 (N/A)questions were1.05 introduced2.49 and specified0 (N/A) with the five-point300 rating1.16 scale from2.84 1 (very dissatisfied) to 5 (very satisfied). The two questions were used to ask the participants after 150 0 (N/A) 1.00 2.61 0 (N/A) 400 1.20 2.80 participating in the HHH and HRH based on both impedance parameter sets as follows: (1) HowThe substantive do you compare HRH theexperiments qualitative were performance successfully of thecarried HHH out to by that the of 20 the selected HRH subjects.using A thecomparison parameter of Set the 1? average interactive force and handover time under the several conditions based on the HRH tasks is given in Table 2. After careful observation with regard to the HRH test results, it was complicated to select the set of optimized impedance parameters as its nature necessitates human judgments. After asking the participants, two desirable choices of the impedance parameters were selected based on the recommendations, which consist of Set 1: 퐵푑(50), 퐾푑 (200) and 푀푑(0.25) and Set 2: 퐵푑(75), 퐾푑(50) and 푀푑(0.25), as highlighted in Table 2 . A questionnaire technique was subsequently Appl. Sci. 2021, 11, x FOR PEER REVIEW 15 of 20

employed to gather information from the human participants. Rating scale questions were introduced and specified with the five-point rating scale from 1 (very dissatisfied) to 5 (very satisfied). The two questions were used to ask the participants after participating in the HHH and HRH based on both impedance parameter sets as follows: Appl. Sci. 2021, 11, 4437 (1) How do you compare the qualitative performance of the HHH to that of the 15HRH of 19 using the parameter Set 1? (2) How do you compare the qualitative performance of the HHH to that of the HRH (2) usingHow the do youparameter compare Set the2? qualitative performance of the HHH to that of the HRH using the parameter Set 2? Table 2. Comparisons of the average interactive force (퐹푖푛푡) and time (푡) parameters, while varying both damping factor Table 2. Comparisons of the average interactive force (F ) and time (t) parameters, while varying both damping factor (퐵푑) and spring stiffness (퐾푑). int (Bd) and spring stiffness (Kd). Varied Damping Factor (푩풅) Varied Spring Stiffness (푲풅) Varied Damping Factor (B ) Varied Spring Stiffness (K ) 푩풅 (NmS/rad) 푲풅 (Nm/rad) 푭풊풏풕d (N) 풕 (s) 푩풅 (NmS/rad) 푲풅 (Nm/rad) 푭풊풏풕 d(N) 풕 (s) Bd (NmS/rad)50 Kd (Nm/rad)0 Fint0.96(N) 1.85t (s) Bd (NmS/rad)75 Kd (Nm/rad)0 1.21Fint (N) 1.62t (s) 50 50 0 0.961.24 2.02 1.85 75 75 500 1.31 1.21 1.86 1.62 50 100 50 1.241.19 1.73 2.02 7575 10050 1.111.31 2.061.86 50 200 100 1.191.31 1.36 1.73 75 75 200 100 1.32 1.11 2.14 2.06 50 200 1.31 1.36 75 200 1.32 2.14 50 300 1.251.25 1.51 1.51 75 75 300 300 1.41 1.41 1.93 1.93 50 400 1.111.11 1.42 1.42 75 75 400 400 1.30 1.30 1.37 1.37

Figure 9 depicts the responses of the participants comparing the overall stability of Figure9 depicts the responses of the participants comparing the overall stability of the robot–human handovers using the different sets of the impedance parameters. It can the robot–human handovers using the different sets of the impedance parameters. It can be observed that 11 participants (55 %) and three participants (15%), respectively, were be observed that 11 participants (55 %) and three participants (15%), respectively, were significantly satisfied and very satisfied with the overall stability of the robot handover significantly satisfied and very satisfied with the overall stability of the robot handover system using the first parameter set. The second set shows 12 (60%) and 6 (30%) subjects system using the first parameter set. The second set shows 12 (60%) and 6 (30%) subjects were satisfied and very satisfied. There is only one person (5%) very dissatisfied with the were satisfied and very satisfied. There is only one person (5%) very dissatisfied with the parameter set 1,1, whilewhile anotheranother leadsleads participants participants to to experience experience the the robot robot handover handover without with- outany any dissatisfaction. dissatisfaction. To compareTo compare the meanthe mean measurements measurements of the of two the settwo undertaken set undertaken from fromthe same the same sample sample group, group, the paired the paired samples samples t-test wast-test statistically was statistically utilized. utilized. The following The following hypotheses H0 and H1 were tested as: hypotheses H0 and H1 were tested as: 퐻0: 휇푆푒푡2 = 휇푆푒푡1, which means the median difference between pairs of observations H0 : µSet2 = µSet1, which means the median difference between pairs of observations isis zero zero so so that that there there is no significant significant difference in the population distributions.distributions. 퐻 : 휇 > 휇 , which means the median difference between pairs of observations H00 : 푆푒푡µSet2 2 > 푆푒푡µSet1 1, which means the median difference between pairs of observations isis not not equal equal to to zero zero and the population rating from set 2 is greater than that of setset 1.1.

FigureFigure 9. SurveySurvey responses responses for the human human–robot–robot handover tasks.

TheThe results results of the statistical analysis based on the paired t--testtest present that ratings of thethe robot robot performance performance based based on on the the parameter parameter set set 1 1 and and 2 2 were were 3.80 3.80 and and 4. 4.20,20, respectively, respectively, with corresponding standard standard deviations deviations of of 0.768 0.768 and and 0.616, 0.616, as as summarized summarized in in Table Table 33.. This represents the test subjects were more appreciative of the performance of the robot behavioural control using the second parameter set rather than the first set. The value of the paired-sample test was −3.559 (2-tails significance = 0.002). It can be described that the hypothesis H0 has to be statistically rejected and the alternative hypothesis H1 accepted. This means that there is a significant difference in the population distributions from the participants. Additionally, the population rating from set 2 is greater than that of set 1, in which the participants were more comfortable with the robot’s stability using the Appl. Sci. 2021, 11, 4437 16 of 19

impedance parameters made up of Kd (50), Bd(75) and Md(0.25), more than those of the ﬁrst set.

Table 3. T-test results of the comparison between the parameter set 1 and 2.

Paired Samples Statistics Mean N SD SD Error Mean Impedence1 3.80 20 0.768 0.172 Pair 1 Impedence2 4.20 20 0.616 0.138 Paired Samples Correlations N Correlation Sig. Pair 1 Impedence1 & Impedence2 20 0.757 0.000 Paired Samples Test Paired Differences Sig. SD Error 95% Conﬁdence of the Difference t df Mean SD (2-tailed) Mean Lower Upper Impedence1 & Pair 1 −0.400 0.503 0.112 −0.635 −0.165 −3.559 19 0.002 Impedence2

Finally, the results of the robotic dynamic response based on the hybrid PID position and impedance control in the robot-to-human handovers were carried out and then compared with the outcomes of the HHH. Figure 10 shows an example of a comparison between the interactive force proﬁles against time from the HHH and HRH tests. It can be concluded that the quantitative measurement of the performance of robot behavioural control can be considered acceptable for object handover interaction with a human receiver. It provided effective performance during the natural cooperative tasks, where the robot was able to successfully pass the object to the human in a safe, reliable and timely manner. However, after careful analysis with regard to the HRH results, the robot has to perform a slightly longer handover period than that of the HHI, even though the maximum forces from both cases are similar. This is because the robot control algorithm was initially de- Appl. Sci. 2021, 11, x FOR PEER REVIEW 17 of 20 signed to ensure its safe and natural operational characteristics speciﬁcally while physically releasing the object to the receiver. Then, this might result longer object handover process.

(a) (b)

FigureFigure 10.10. InteractiveInteractive forceforce proﬁlesprofiles againstagainst thethe timetime ofof ((aa)) the the HHH HHH test test and and ( b(b)) the the HRH HRH test. test.

4. Conclusions This paper contributes to the implementation of a human-like behavioural control strategy for seamless HRH. The robotic conceptual framework was established by understanding the principle of human haptic interaction when two humans work together in a joint effort to complete an object handover task. The performance of the robot–human handover system has been evaluated based on the interactive fore profiles, handover time and how comfortable the participants felt in participating in the HRH tasks. The results provide effective performance during the HRH, where the HSR robot was able to successfully pass the object to the human effectively. The safety systems proposed are working successfully and thus avoiding the likelihood of unsafe HRH actions being taken. Moreo- ver, the feedback responses from the human subjects participating in the HRI also agreed with the parallel test results that gave more satisfaction with a higher average rating scale. Therefore, it can be concluded that the robot based on the developed behavioural control algorithm is capable of tracking the object transfer location, manipulating the object and handling it to the receiver in a safe, reliable and dexterous manner. This developed robotic system can enhance the accuracy of positioning and force regulating abilities in the HRH system and also increase the smoothness of physical HRI. The outcomes of this research can successfully play a beneficial role in the daily lives of people in future.

Author Contributions: Conceptualization, P.N.; methodology, P.N. and T.S.; software, T.S.; validation, P.N. and T.S.; formal analysis, P.N. and T.S.; investigation, P.N. and T.S.; resources, P.N.; data curation, P.N. and T.S.; writing—original draft preparation, P.N.; writing—review and editing, P.N.; visualization, P.N.; supervision, P.N.; project administration, P.N.; funding acquisition, P.N. All authors have read and agreed to the published version of the manuscript Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.

References 1. Goodrich, M.; Schultz, A. Human-Robot Interaction: A Survey. Found. Trends Hum. Comput. Interact. 2007, 1, 203–275. Appl. Sci. 2021, 11, 4437 17 of 19

Consequently, it can be concluded that understanding kinematically and dynamically how two humans physically collaborate while naturally performing object handover tasks offers the effective performance and reliability of the robot-to-human handover procedure and acceptable HRI implementation. This was in agreement with the parallel test outcomes, demonstrating signiﬁcant satisfaction with the overall performance of the HRH system, as measured by an average rating of 4.20 on a ﬁve-point scale.

4. Conclusions This paper contributes to the implementation of a human-like behavioural control strategy for seamless HRH. The robotic conceptual framework was established by understanding the principle of human haptic interaction when two humans work together in a joint effort to complete an object handover task. The performance of the robot–human handover system has been evaluated based on the interactive fore proﬁles, handover time and how comfortable the participants felt in participating in the HRH tasks. The results provide effective performance during the HRH, where the HSR robot was able to successfully pass the object to the human effectively. The safety systems proposed are working successfully and thus avoiding the likelihood of unsafe HRH actions being taken. Moreover, the feedback responses from the human subjects participating in the HRI also agreed with the parallel test results that gave more satisfaction with a higher average rating scale. Therefore, it can be concluded that the robot based on the developed behavioural control algorithm is capable of tracking the object transfer location, manipulating the object and handling it to the receiver in a safe, reliable and dexterous manner. This developed robotic system can enhance the accuracy of positioning and force regulating abilities in the HRH system and also increase the smoothness of physical HRI. The outcomes of this research can successfully play a beneﬁcial role in the daily lives of people in future.

Author Contributions: Conceptualization, P.N.; methodology, P.N. and T.S.; software, T.S.; validation, P.N. and T.S.; formal analysis, P.N. and T.S.; investigation, P.N. and T.S.; resources, P.N.; data curation, P.N. and T.S.; writing—original draft preparation, P.N.; writing—review and editing, P.N.; visualization, P.N.; supervision, P.N.; project administration, P.N.; funding acquisition, P.N. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References 1. Goodrich, M.; Schultz, A. Human-Robot Interaction: A Survey. Found. Trends Hum. Comput. Interact. 2007, 1, 203–275. [CrossRef] 2. Shafkat Tanjim, M.S.; Raﬁ, S.A.; Oishi, A.N.; Barua, S.; Dey, H.C.; Babu, M.R. Image Processing Intelligence Analysis for Robo-Res 1.0: A Part of Humanoid Rescue-Robot. In Proceedings of the 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 5–7 June 2020; pp. 1229–1232. 3. Zhao, J.; Gao, J.; Zhao, F.; Liu, Y. A Search-and-Rescue Robot System for Remotely Sensing the Underground Coal Mine Environment. Sensors 2017, 17, 2426. [CrossRef][PubMed] 4. Cardona, G.A.; Calderon, J.M. Robot Swarm Navigation and Victim Detection Using Rendezvous Consensus in Search and Rescue Operations. Appl. Sci. 2019, 9, 1702. [CrossRef] 5. e Silva, L.C.; Daher, S.F.D.; Santiago, K.T.M.; Costa, A.P.C.S. Selection of an Integrated Security Area for locating a State Military Police Station based on MCDM/A method. In Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 6–9 October 2019; pp. 1530–1534. 6. Boisboissel, G. Is it sensible to grant autonomous decision-making to military robots of the future? In Proceedings of the 2017 International Conference on Military Technologies (ICMT), Brno, Czech Republic, 31 May–2 June 2017; pp. 738–742. Appl. Sci. 2021, 11, 4437 18 of 19

7. Rosli, R.; Abdullah, M.; Siregar, N.C.; Hamid, N.S.A.; Abdullah, S.; Beng, G.K.; Halim, L.; Daud, N.M.; Bahari, S.A.; Majid, R.A.; et al. Exploring Space Science through the UKM-SIDn Outreach Program. In Proceedings of the 2019 International Conference on Space Science and Communication (IconSpace), Johor Bahru, Malaysia, 28–30 July 2019; pp. 253–256. 8. Xing, Z.; Zhao, Y.; Zhu, S. Path Planning Method Design and Dynamic Model Simplification of Free-Flying Space Robot. In Proceedings of the 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), Kristiansand, Norway, 9–13 November 2020; pp. 1501–1505. 9. Islam, M.R.; Chowdhury, F.H.; Rezwan, S.; Ishaque, M.J.; Akanda, J.U.; Tuhel, A.S.; Riddhe, B.B. Novel design and performance analysis of a Mars exploration robot: Mars rover mongol pothik. In Proceedings of the 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, India, 3–5 November 2017; pp. 132–136. 10. Javaid, M.; Estivill-Castro, V. Explanations from a Robotic Partner Build Trust on the Robot’s Decisions for Collaborative Human-Humanoid Interaction. Robotics 2021, 10, 51. [CrossRef] 11. Katsanis, I.A.; Moulianitis, V.C. An Architecture for Safe Child–Robot Interactions in Autism Interventions. Robotics 2021, 10, 20. [CrossRef] 12. Mi, J.; Takahashi, Y. Whole-Body Joint Angle Estimation for Real-Time Humanoid Robot Imitation Based on Gaussian Process Dynamical Model and Particle Filter. Appl. Sci. 2020, 10, 5. [CrossRef] 13. Jung, S.E.; Won, E.-S. Systematic Review of Research Trends in Robotics Education for Young Children. Sustainability 2018, 10, 905. [CrossRef] 14. Aa Diyas, Y.; Brakk, D.; Aimambetov, Y.; Sandygulova, A. Evaluating peer versus teacher robot within educational scenario of programming learning. In Proceedings of the 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Christchurch, New Zealand, 7–10 March 2016; pp. 425–426. 15. Stuhlenmiller, F.; Weyand, S.; Jungblut, J.; Schebek, L.; Clever, D.; Rinderknecht, S. Impact of Cycle Time and Payload of an Industrial Robot on Resource Efficiency. Robotics 2021, 10, 33. [CrossRef] 16. Tao, Y.; Chen, C.; Wang, T.; Chen, Y.; Xiong, H.; Ren, F.; Zou, Y. A Re-Entry Path Planning Method for Service Robots Based on Dynamic Inver-Over Evolutionary Algorithm. Appl. Sci. 2020, 10, 305. [CrossRef] 17. Kupcsik, A.; Hsu, D.; Lee, W.S. Learning Dynamic Robot-to-Human Object Handover from Human Feedback. In Robotics Research; Bicchi, A., Burgard, W., Eds.; Springer Proceedings in Advanced Robotics; Springer: Berlin/Heidelberg, Germany, 2018; Volume 2, pp. 161–176. 18. Neranon, P. Implicit force control approach for Safe physical Robot-to-Human object handover. Indones. J. Electr. Eng. Comput. Sci. 2020, 17, 615–628. [CrossRef] 19. He, W.; Sidobre, D. Improving human-robot object exchange by online force classification. J. Hum. Robot Interact. 2015, 4, 75–94. [CrossRef] 20. Strabala, K.; Lee, M.K.; Dragan, A.; Forlizzi, J.; Srinivasa, S.S.; Cakmak, M.; Micelli, V. Toward Seamless Human–Robot Handovers. J. Hum. Robot Interact. 2013, 2, 112–132. [CrossRef] 21. Emmelmann, M. Influence of velocity on the handover delay associated with a radio-signal-measurement-based handover decision. In Proceedings of the VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, Dallas, TX, USA, 28–30 September 2005; pp. 2282–2286. 22. Neranon, P. Human-to-Robot Object Handover using a Behavioural Position-based Force Control Approach. In Proceedings of the First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP), Bangkok, Thailand, 16–18 January 2019; pp. 5–8. [CrossRef] 23. Huber, M.; Radrich, H.; Wendt, C.; Rickert, M.; Knoll, A.; Brandt, T. Evaluation of a novel biologically inspired trajectory generator in human-robot interaction. In Proceedings of the RO-MAN 2009—The 18th IEEE International Symposium on Robot and Human Interactive Communication, Toyama, Japan, 27 September–2 October 2009; pp. 639–644. 24. Kajikawa, S.; Okino, T.; Ohba, K.; Inooka, H. Motion planning for hand-over between human and robot. In Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems, Pittsburgh, PA, USA, 5–9 August 1995; pp. 193–199. 25. Chan, W.P.; Parker, C.A.; Van der Loos, H.F.; Croft, E.A. Grip forces and load forces in handovers: Implications for designing human-robot handover controllers. In Proceedings of the 7th Annual ACM/IEEE International Conference on Human-Robot Interaction, Boston, MA, USA, 2–5 March 2012; pp. 9–16. 26. Baier, T.; Zhang, J. Reusability-based semantics for grasp evaluation in context of service robotics. Robotics and Biomimetics. In Proceedings of the 2006 IEEE International Conference on Robotics and Biomimetics (ROBIO’06), Kunming, China, 17–20 December 2006; pp. 703–708. 27. Nemlekar, H.; Dutia, D.; Li, Z. Object Transfer Point Estimation for Fluent Human-Robot Handovers. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20–24 May 2019; pp. 2627–2633. [CrossRef] 28. Lemic, F.; Behboodi, A.; Famaey, J.; Mathar, R. Location-Based Discovery and Vertical Handover in Heterogeneous Low-Power Wide-Area Networks. IEEE Internet Things J. 2019, 6, 10150–10165. [CrossRef] 29. Cakmak, M.; Srinivasa, S.; Lee, M.K.; Forlizzi, J.; Kiesler, S. Human preferences for robot-human hand-over configulations. In Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco, CA, USA, 25–30 September 2011; pp. 1986–1993. Appl. Sci. 2021, 11, 4437 19 of 19

30. Chan, W.P.; Pan, M.K.; Croft, E.A.; Inaba, M. Characterization of handover orientations used by humans for efficient robot to human handovers. In Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 28 September–2 October 2015; pp. 1–6. 31. Aleotti, J.; Micelli, V.; Caselli, S. An affordance sensitive system for robot to human object handover. Int. J. Soc. Robot. 2014, 6, 653–666. [CrossRef] 32. Sutiphotinun, T.; Neranon, P.; Vessakosol, P.; Romyen, A.; Hiransoog, C.; Sookgaew, J. A Human-Inspired Control Strategy: A Framework for Seamless Human-Robot Handovers. J. Mech. Eng. Res. Dev. 2020, 43, 235–245. 33. Kato, S.; Yamanobe, N.; Venture, G.; Yoshida, E.; Ganesh, G. The where of handovers by humans: Effect of partner characteristics, distance and visual feedback. PLoS ONE 2019, 14, e0217129. [CrossRef] 34. Chien, M.H.; Maya, C.; Bilge, M. Adaptive Coordination Strategies for Human-Robot Handovers. In Proceedings of the Robotics: Science and Systems, Rome, Italy, 13–17 July 2015; pp. 1–10. 35. Sisbot, E.A.; Alami, R. A human-aware manipulation planner. IEEE Trans. Robot. 2012, 28, 1045–1057. [CrossRef] 36. Glasauer, S.; Huber, M.; Basili, P.; Knoll, A.; Brandt, T. Interacting in time and space: Investigating human–human and human– robot joint action. In Proceedings of the 19th International Symposium in Robot and Human Interactive Communication, Viareggio, Italy, 13–15 September 2010; pp. 252–257. [CrossRef] 37. Kim, J.; Park, J.; Hwang, Y.K.; Lee, M. Advanced Grasp Planning for Handover Operation Between Human and Robot: Three Handover Methods in Esteem Etiquettes Using Dual Arms and Hands of Home-Service Robot. In Proceedings of the 2nd International Conference on Autonomous Robots and Agents, Palmerston North, New Zealand, 12–14 December 2004; pp. 34–39. 38. Huber, M.; Rickert, M.; Knoll, A.; Brandt, T.; Glasauer, S. Human-robot interaction in handing-over tasks. Robot and Human Interactive Communication, 2008. RO-MAN 2008. In Proceedings of the 17th IEEE International Symposium, Munich, Germany, 1–3 August 2008; pp. 107–112. 39. Jae-Bong, Y.; Taewoong, K.; Dongwoon, S.; Seung-Joon, Y. Unified Software Platform for Intelligent Home Service Robots. Appl. Sci. 2020, 10, 5874. [CrossRef] 40. Denavit, J.; Hartenberg, R.S. A kinematic notation for lower-pair mechanisms based on matrices. J. Appl. Mech. 1955, 22, 215–221. 41. Yamamoto, T.; Terada, K.; Ochiai, A. Development of Human Support Robot as the research platform of a domestic mobile manipulator. Robomech. J. 2019, 6.[CrossRef] 42. Jaroonsorn, P.; Neranon, P.; Smithmaitrie, P.; Dechwayukul, C. Robot-assisted transcranial magnetic stimulation using hybrid position/force control. Adv. Robot. 2020, 34, 1559–1570. [CrossRef] 43. Raibert, M.H.; Craig, J.J. Hybrid Position/Force Control of Manipulators. J. Dyn. Syst. Meas. Control 1981, 103, 126–133. [CrossRef] 44. Volpe, R.; Khosla, P. A theoretical and experimental investigation of explicit force control strategies for manipulators. IEEE Trans. Autom. Control 1993, 38, 1634–1650. [CrossRef] 45. Neranon, P.; Bicker, R. Force/position control of a robot manipulator for human-robot interaction. Therm. Sci. Int. Sci. J. 2016, 20, 537–548. [CrossRef] 46. Rahman, M.M.; Ikeura, R.; Mizutani, K. Investigation of the impedance characteristic of human arm for development of robots to cooperate with humans’. JSME Int. J. Ser. C 2002, 45, 510–518. [CrossRef] 47. Vukobratovic, M.; Surdilovic, D.; Ekalo, Y. Dynamics and Robust Control of Robot-environment Interaction. In New Frontiers in Robotics; World Scientific Publishing Co. Pte. Ltd.: Singapore, 2008; Volume 2. 48. Matthias, B.; Reisinger, T. Example Application of ISO/TS 15066 to a Collaborative Assembly Scenario. In Proceedings of the 47th International Symposium on Robotics: ISR 2016, Munich, Germany, 21–22 June 2016; pp. 1–5. 49. Vasic, M.; Billard, A. Safety Issues in Human-Robot Interactions. In Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, 6–10 May 2013; pp. 197–204. [CrossRef] 50. Duchemin, G.; Poignet, P.; Dombre, E.; Peirrot, F. Medically safe and sound [human-friendly robot dependability]. IEEE Robot. Autom. Mag. 2004, 11, 46–55. [CrossRef] 51. Kulic, D.; Croft, E.A. Real-time safety for human–robot interaction. Robot. Auton. Syst. 2006, 54, 1–12. [CrossRef] 52. Ruslan, F.A.; Haron, K.A.; Samad, M.; Adnan, R. Multiple Input Single Output (MISO) ARX and ARMAX model of flood prediction system: Case study Pahang. In Proceedings of the 2017 IEEE 13th International Colloquium on Signal Processing & Its Applications: CSPA, Penang, Malaysia, 10–12 March 2017; pp. 179–184. [CrossRef] 53. Rabbani, F. Model Identification and Validation for a Heating System using MATLAB System Identification Toolbox. In Proceedings of the 1st International Conference on Sensing for Industry, Control, Communications, & Security Technologies (ICSICCST 2013), Karachi, Pakistan, 24–26 June 2013.