A Mechatronics-Embedded Pneumatic Soft Modular Robot Powered Via Single Air Tube

applied sciences

Article A Mechatronics-Embedded Pneumatic Soft Modular Robot Powered via Single Air Tube

Yu Zhang 1, Yubin Liu 1,*, Xin Sui 1, Tianjiao Zheng 1, Dongyang Bie 2, Yulin Wang 1, Jie Zhao 1 and Yanhe Zhu 1,*

1 State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China; [email protected] (Y.Z.); [email protected] (X.S.); [email protected] (T.Z.); [email protected] (Y.W.); [email protected] (J.Z.) 2 The Institute of Robotics and Automatic Information Systems, College of Computer and Control Engineering, and the Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300071, China; [email protected] * Correspondence: [email protected] (Y.L.); [email protected] (Y.Z.)

Received: 22 April 2019; Accepted: 30 May 2019; Published: 31 May 2019

Abstract: Soft modular robots have advantages, including infinite degrees of freedom and various configurations. Most soft robots are actuated by inflating air pressure into their chambers. However, each chamber is connected to a tube that provides the air supply, which incurs drag and intertwining problems that influence the robot’s motion. Moreover, the number of chambers directly affects the deformations and motion capabilities of the robot. Therefore, the crucial issue is the structure of a soft modular robot that can share an air source without reducing the number of chambers and can guarantee the deformations of the robot. In this paper, a novel mechatronics-embedded soft module was designed and manufactured, which has an air supply sharing function. Therefore, the soft modular robot can be powered via a single air tube. In addition, a wireless platform to control the air pressure of the module was built, and an experimental model was established to obtain the relationship between the deformation and pressure of the module. Four experiments were performed under different conditions. The experiments’ results indicate the bending capability of the module. Moreover, hooking object, twisting motion, and bionic gesture experiments demonstrate the validity of the module’s air pressure sharing function. Therefore, the air sharing supply approach proposed in this paper can be used as a reference to solve the tube drag problem of soft modular robots.

Keywords: soft modular robot; mechatronics-embedded soft module; air pressure sharing; bionic motion

1. Introduction The concept of modular robots has been proposed since the 1980s to solve the universal property and flexibility problems of robots with fixed configurations [1]. A modular robot consists of unified modules with the same structure and function to realize reconfigurable configurations for adapting to various tasks and changeable environments. The unified module has communication, controller, actuator, sensor and connecting mechanisms, which can connect with each other to form different configurations. Many researchers have developed rigid modular robots, including Molecubes [2], Yamor [3], Polybot [4], ATRON [5], CONRO [6], M-TRAN [7], SuperBot [8], UBot [9,10], and Seremo [11]. These modular robots can alter their structure by connecting the different faces of their modules. However, rigid modular robots have finite degrees of freedom, and rigid materials are dangerous for fragile objects and operating personnel. Therefore, the use of soft materials to replace rigid materials to manufacture modular robots is a novel research direction.

Appl. Sci. 2019, 9, 2260; doi:10.3390/app9112260 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 2260 2 of 13

Soft modular robots have advantages including infinite degrees of freedom and various configurations [12]. Soft modules are manufactured using soft materials, such as silica gel or rubber, which have characteristics of flexibility and continuous deformation. Therefore, many researchers have studied soft modular robots, including omnidirectional soft robots [13], rotary soft module and bending soft module [14], three types of pneumatically actuated soft modules (SoBL) [15], the soft modular robot with connecting part [16], elastomeric hollow cubes with magnetic connecting mechanisms [17], bidirectional bending modules [18], pneumatic actuators with four chambers [19], multi-spherical modular soft robots [20–23], bending-type fluidic elastomer actuators [24], modular soft robots with voice coil actuators [25], soft-bodied caterpillar-like modular robots [26], and vacuum-powered soft pneumatic actuator (V-SPA) modules [27,28]. Some researchers have concentrated their attention on the structure of the soft module. Kurumaya presented the rotary soft module and bending soft module, which can be combined with other actuators as part of a complete manipulator system [14]. Lee designed three types of pneumatically actuated soft modules, which can connect with each other through a unified connecting part [15]. Zhang manufactured and presented a pneumatic soft modular robot with a novel connecting mechanism to achieve a docking function [16]. Luo designed a chamber’s structure of soft module, which has a bidirectional bending ability [24]. Meanwhile, a number of researchers have concentrated on the soft modular robot’s bionic motion. Jun proposed a reconfigurable omnidirectional soft robot based on the locomotion of caterpillars. The pneumatic soft robot is capable of moving with three DOFs (degree of freedoms) on a plane [13]. Vergara proposed a new soft modular robot to simulate replicating morphogenetic movements of an embryo, which provide tangible models of cell behavior [17]. Hamill manufactured a soft module with four chambers and analyzed the gait of a soft hexapod robot [19]. Fei presented a multi-sphere modular robot to realize an obstacle avoidance motion [21]. Umedachi proposed a soft-bodied caterpillar-like modular robot to simulate caterpillar motion [25]. Nevertheless, most soft modular robots are actuated by inflating air pressure. Therefore, each chamber of the soft module needs to be connected with an air tube, which provides air pressure to actuate the deformation of the module. However, air tubes incur drag and intertwining problems that affect the motions of the soft modular robots. Moreover, the problems cannot be solved by reducing the number of chambers, which directly affects the deforming ability of the soft robot. Therefore, it is critical to design the soft modular robot with a shared air source and guarantee the deformations. A few researchers have investigated the gas source sharing issue. Gunjan Agarwal proposed a vacuum-powered soft pneumatic actuator (V-SPA), which shared a vacuum power supply [27,28]. However, the V-SPA has a novel solenoid valve, manufactured by piezoelectric ceramic technology, which is a non-universal technology for researchers to manufacture soft modular robots. Therefore, we present a mechatronics-embedded pneumatic soft modular robot (MeSMR) powered via single air tube. The MeSMR provides a common and convenient approach to design and manufacture of soft modular robot. There are five sections in this paper. Section2 introduces the design and manufacture process of the soft module. Control systems and experimental modelling are presented in Section3. Section4 illustrates experiments and results of the MeSMR.

2. Design and Manufacture of Soft Module

2.1. Structure and Manufacture of Soft Module Soft modular robots mostly utilize solenoid valves that open or close to provide air-flow. Therefore, soft modular robots need air tubes connected to chambers. Moreover, air tubes easily twine around each other, which greatly affect the movement of soft robots. However, solenoid valves are too large to be placed into a soft module. To solve the problem of excessive tubes, a novel structure for the soft module is designed in this section. Appl. Sci. 2018, 8, x FOR PEER REVIEW 3 of 14 wireless control board system, a battery and three gas flow valves. The gas flow valve is composed of a motor base, a reduction motor, an external thread connection, an internal thread sleeve and a silica gel plug. The reduction motor is controlled by the control board, which drives the external thread to facilitate the silica gel plug’s linear motion along the motor base. Three fan-shaped chambers are blocked by the silica gel plugs when the motor is unpowered. When the control board receives a wireless signal, the output shaft of the reduction motor connects to the screw and enables the linear motion of the silica gel plug. Then, the air pressure inflates the fan-shaped chamber to realize deformation. The silica gel module (Part B) has four chambers. Three fan-shaped chambers are used to render the module’s deformation, and one cylindrical chamber is used to transport air pressure for the fan- Appl.shaped Sci. 2019chambers., 9, 2260 The cylindrical chamber’s diameter is denoted as d, which is on the center of3 ofthe 13 module. The angle between any two connecting lines of the fan-shaped chambers’ center points is 120°.Figure Other1 designshows theparameters manufacture of the process modu ofle the are soft listed module, as follows: which includesThe diameter a gas controller, D , the silicaangle betweengel module the and two gas sides supply of the connection. sector q, the The arcs gas of controller the sector (Part R and A)consists r . The ofmodule a seal is cover, made a by wireless silica gelcontrol demolding board system, technology. a battery and three gas flow valves. The gas flow valve is composed of a motor base,The a reduction gas supply motor, connection an external (Part thread C) has connection, one internal an internalthread hole thread in each sleeve face, and which a silica is gelused plug. to Theconnect reduction with the motor threaded is controlled parts. byThe the threaded control board, parts whichhave three drives types the external of structure: thread Hermetical, to facilitate fastener,the silica and gel plug’s ventilated linear parts. motion The along hermetical the motor part base. is used Three to seal fan-shaped the internal chambers thread arehole. blocked The fastener by the issilica used gel to plugs connect when with the two motor gas supply is unpowered. connections When. One the end control of the board ventilated receives part a wireless connects signal, with the internaloutput shaft thread of thehole, reduction and the motorother end connects of the to ventilat the screwed part and is enables used to the connect linear motionwith the of air the tube. silica The gel plug.gas supply Then, connection the air pressure has six inflates connecti theng fan-shaped faces. One chamber of the connecting to realize deformation. faces is used to fix the silica gel module, and the other connecting faces are used to connect with the other modules or air tube.

Figure 1. Manufacture process for the soft module.

The silica gel module (Part B) has four chambers. Three fan-shaped chambers are used to render the module’s deformation, and one cylindrical chamber is used to transport air pressure for the fan-shaped chambers. The cylindrical chamber’s diameter is denoted as d, which is on the center of the module. The angle between any two connecting lines of the fan-shaped chambers’ center points is 120◦. Other design parameters of the module are listed as follows: The diameter D, the angle between the two sides of the sector q, the arcs of the sector R and r. The module is made by silica gel demolding technology. The gas supply connection (Part C) has one internal thread hole in each face, which is used to connect with the threaded parts. The threaded parts have three types of structure: Hermetical, fastener, and ventilated parts. The hermetical part is used to seal the internal thread hole. The fastener is used Appl. Sci. 2018, 8, x FOR PEER REVIEW 4 of 14

The gas controller and gas supply connection are bonded on the two ends of the silica gel module by utilizing Smooth-On silicone rubber adhesive. The molds and gas supply connection are manufactured by 3-D printing technology. Therefore, the modules are connected by the gas supply connection and threaded parts, which can form various configurations and have air pressure sharing functions.

2.2. Parameter Optimization of the Soft Module To design a soft module with satisfactory performance, the relationship between the structure parameters and the deformation of the module is a key factor. Therefore, the material characteristics are obtained by uniaxial tensile tests with a 1 cm square length silica gel slice. The stress and strain are obtained and shown in Table 1. Then, the uniaxial tensile results and Workbench software’s Yeoh model are used to fit the deformation curve. Therefore, the parameters of deformation energy = = C1 0.036 MPa and C 2 0.007 MPa are obtained, and the fitting curves are shown in Figure 2.  D = 22 L=95 q = 90 TheAppl. parameters Sci. 2019, 9, 2260 of the soft module mm, mm, and are defined.4 of 13 The other parameters of the module are obtained by contrasting the deformations with various conditions.

Figure to3–5 connect illustrate with two the gas module’s supply connections. bending One angles end of with the ventilated different part parameters. connects with Figure the internal 3 shows an increasethread in the hole, bending and the angles other end with of theradius ventilated R of part the is fan-shaped used to connect chamber. with the Moreover, air tube. The the gas increasing rate of supplythe bending connection angle has of six the connecting module faces. at R One=8.5 of the mm connecting is higher faces than is usedthat toof fix the the other silica gelparameters. The increasingmodule, and rate the of other the module connecting with faces the are radius used to connectr of the with fan-shaped the other modules chamber or air are tube. shown in Figure The gas controller and gas supply connection are bonded on the two ends of the silica gel module by 4. However, the diameter d of the cylindrical chamber is limited to the radius r . Figure 5 shows utilizing Smooth-On silicone rubber adhesive. The molds and gas supply connection are manufactured that theby effect 3-D printing of the diameter technology. d Therefore, on the bending the modules angle are connectedof the module by the is gas small. supply Moreover, connection andthe thickness of the threadedmodule parts,is too which slim can when form variousthe diameter configurations d is and too have large air pressure and the sharing radius functions. r is too small to R=8.5 r =3 d =3 manufacture.2.2. Parameter Therefore, Optimization the offo thellowing Soft Module parameters are chosen: mm, mm and mm. To design a soft module with satisfactory performance, the relationship between the structure To prove the rationality of the parameter selection, we used the Abaqus software to simulate the parameters and the deformation of the module is a key factor. Therefore, the material characteristics bendingare states obtained of bythe uniaxial module tensile with tests optimized with a 1 cm parameters. square length Figure silica gel 6 slice. shows The an stress increase and strain in arethe bending angles obtainedof the module and shown with in Table air 1pressure,. Then, the and uniaxial the tensile bend resultsing angle and Workbench is 180° when software’s the air Yeoh pressure model 50 kPa. Therefore,are used the tosoft fit themodule deformation has an curve. appropriat Therefore,e thebending parameters ability of deformation with the designed energy C1 = structure.0.036 MPa and C2 = 0.007 MPa are obtained, and the fitting curves are shown in Figure2. Table 1. The results of uniaxial tensile tests with a silica gel slice. Table 1. The results of uniaxial tensile tests with a silica gel slice.

Stress(MPa)Stress(MPa) 0.0400.040 0.055 0.055 0.073 0.073 0.1010.101 0.1270.1270.141 0.141 0.163 0.163 0.206 0.206 0.233 0.233 0.253 0.253 Strain(mm)Strain(mm) 0.0780.078 0.294 0.294 0.509 0.509 0.7340.734 0.9680.9681.082 1.082 1.207 1.207 1.431 1.431 1.676 1.676 1.874 1.874

Uniaxial tension simulation data 1 Biaxial drawing simulation data Plane shear simualtion data 0.8 Uniaxial tension test results

0.6

0.4

0.2

-0.2 00.511.52 Strain (mm) Figure 2. The ﬁtting curves by the Yeoh model. Figure 2. The fitting curves by the Yeoh model.

The parameters of the soft module D = 22 mm, L = 95 mm, and q = 90◦ are defined. The other parameters of the module are obtained by contrasting the deformations with various conditions. Figures3–5 illustrate the module’s bending angles with di fferent parameters. Figure3 shows an increase in the bending angles with radius R of the fan-shaped chamber. Moreover, the increasing rate of the bending angle of the module at R = 8.5 mm is higher than that of the other parameters. The increasing rate of the module with the radius r of the fan-shaped chamber are shown in Figure4. However, the diameter d of the cylindrical chamber is limited to the radius r. Figure5 shows that the effect of the diameter d on the bending angle of the module is small. Moreover, the thickness of the module is too slim when the diameter d is too large and the radius r is too small to manufacture. Therefore, the following parameters are chosen: R = 8.5 mm, r = 3 mm and d = 3 mm. To prove the rationality of the parameter selection, we used the Abaqus software to simulate the bending states of the module with optimized parameters. Figure6 shows an increase in the bending angles of the module with air pressure, and the bending angle is 180◦ when the air pressure 50 kPa. Therefore, the soft module has an appropriate bending ability with the designed structure. Appl. Sci. 2018, 8, x FOR PEER REVIEW 5 of 14 Appl. Sci. 2018, 8, x FOR PEER REVIEW 5 of 14 Appl. Sci. 2019, 9, 2260 5 of 13 Appl. Sci. 2018, 8, x FOR PEER REVIEW 5 of 14 Angle (°) Angle (°) Angle (°)

R Figure 3. Bending angles of the soft model with the radius . Figure 3. Bending angles of the soft model with the radius R . FigureFigure 3. 3.Bending Bending anglesangles ofof thethe soft model with the the radius radius RR. . Angle (°) Angle (°) Angle (°)

r Figure 4. Bending angles of the soft model with the radius . FigureFigure 4. 4.Bending Bending anglesangles ofof thethe softsoft modelmodel with the radius rr. . Figure 4. Bending angles of the soft model with the radius r . 180 d=2mm 160180 d=2.5mm d=3mmd=2mm 180140160 d=2.5mm d=2mm d=3mm 160120140 d=2.5mm d=3mm 140100120 12010080 1006080 804060 602040 40200 0 1020304050 200 Pressure (kPa)) 0 1020304050 Appl. Sci. 2018, 8, x FOR PEER REVIEW 6 of 14 0 Pressure (kPa)) FigureFigure 5. 5.Bending Bending0 anglesangles 1020304050 ofof thethe soft model with the the diameter diameter dd. . Pressure (kPa)) Figure 5. Bending angles of the soft model with the diameter d . Figure 5. Bending angles of the soft model with the diameter d .

FigureFigure 6. 6.The The bendingbending statesstates of the module by Abaqus Abaqus software software simulation. simulation.

3. Control System and Experimental Model

3.1. Control System for the MeSMR The soft modules are actuated by the inflating air pressure. Therefore, air pressure is a very important parameter to influence the deformation of the module. In order to obtain the relationship between pressure and deformation of the soft module, a closed loop control system is established using a pressure sensor. The experiment platform has an inflating/deflating function, as shown in Figure 7. The dash frame is the control system for the MeSMR, which includes DC power, PCB, Wi- Fi module, air pump, solenoid valves, and controller. The control system has a quick-speed inflating function and a slow-speed inflating function. The quick-speed inflating function is used when the pressure is far below the desired value (greater than 5 kPa). The slow-speed function is used for precise control when the pressure of the module is close to the desired value (smaller than 5 kPa). Therefore, the control principle of a platform based on proportional–integral–derivative (PID) theory is Δ=×−−+×+×−×−+− uk() kpid (() ek ek ( 1)) k ek () k (() ek 2 ek ( 1) ek ( 2)) (1)

where Δuk() is the output increment of the controller on the kth sampling period; ek() is the

th pressure error on the k sampling period; and kp , ki and kd are the proportion coefficient, integral constant, and differential constant of the PID controller, respectively. = = = The controller’s parameters are kp 50 , ki 10 , and kd 0 when working in a quick-speed = = = state. The parameters of the slow-speed state are kp 100 , ki 20 , and kd 0 . Figure 8 shows the curves of the input pressure and actual pressure of the PID controller. The maximum error of the stable pressure is 0.9029 kPa, which illustrates the validity of the controller. Appl. Sci. 2019, 9, 2260 6 of 13

3. Control System and Experimental Model

3.1. Control System for the MeSMR The soft modules are actuated by the inflating air pressure. Therefore, air pressure is a very important parameter to influence the deformation of the module. In order to obtain the relationship between pressure and deformation of the soft module, a closed loop control system is established using a pressure sensor. The experiment platform has an inflating/deflating function, as shown in Figure7. The dash frame is the control system for the MeSMR, which includes DC power, PCB, Wi-Fi module, air pump, solenoid valves, and controller. Appl. Sci. 2018, 8, x FOR PEER REVIEW 7 of 14

FigureFigure 7. The 7. Experiment The Experiment platform: platform: (a ()a) Experiment Experiment platform; platform; (b) ( controlb) control system system for the for MeSMR. the MeSMR.

The control system has a quick-speed50 inflating function and a slow-speed inflating function. The Desired values quick-speed inflating function is usedActual when values the pressure is far below the desired value (greater than 40 5 kPa). The slow-speed function is used for precise control when the pressure of the module is close to the desired value (smaller than 530 kPa).

Therefore, the control principle20 of a platform based on proportional–integral–derivative (PID) theory is 10

∆u(k) = kp (e(k) e(0 k 1)) + k e(k) + k (e(k) 2 e(k 1) + e(k 2)) (1) × − 0− 50i 100× 150d × 200 250− × − − Step where ∆u(k) is the output increment of the controller on the kth sampling period; e(k) is the pressure th Figure 8. Comparison of the desired and actual values of the control system. error on the k sampling period; and kp, ki and kd are the proportion coefficient, integral constant, and differentialThe constant soft module of the PIDshowed controller, a well respectively.repeatable performance as shown in Figure 9. In these = = = Theexperiments, controller’s the parameters soft module are wask pinflated50, kfromi 100 kPa, and tok 60d kPa,0 when and then working deflated in to a quick-speed0 kPa. The state. results, which are shown in the same color, =are obtained= in a one-time= experiment. Meanwhile, the The parameters of the slow-speed state are kp 100, ki 20, and kd 0. Figure8 shows the curves of the inputexperiments pressure were and actualrepeated pressure three time ofs thewith PID the same controller. conditions. The The maximum hysteretic error characteristic of the stable is also pressure presented in Figure 9. The bending angles in the process of decreasing pressure are larger than those is 0.9029 kPa, which illustrates the validity of the controller. in the process of increasing pressure, which is caused by properties of the silica gel. Meanwhile, the Thelargest soft modulehysteretic showed angle is a5.5 well degrees. repeatable performance as shown in Figure9. In these experiments, the soft moduleFigure was 10 illustrates inflated the from 2D workspace 0 kPa to 60of the kPa, soft and module, then which deflated is the to longitudinal 0 kPa. The section results, of which are shownits 3D in workspace, the same forming color, arean area obtained enclosed in by a two one-time curves. The experiment. edges of the Meanwhile, 3D workspace theof the experiments soft were repeatedmodule are three two times surfaces with shown the samein Figure conditions. 11. The maximum The hysteretic displacement characteristic in the radial is direction also presented is in Figure924. The mm, bending and the farthest angles and in nearest the process points of in decreasingthe axial direction pressure are 190 are mm larger and 229 than mm, those respectively. in the process of increasing pressure, which is caused by properties of the silica gel. Meanwhile, the largest hysteretic Increasing pressure angle is 5.5 degrees. Decreasing pressure Figure 10 illustrates the 2D workspace of the soft module, which is the longitudinal section of its 3D workspace, forming an area enclosed by two curves. The edges of the 3D workspace of the soft module are two surfaces shown in Figure 11. The maximum displacement in the radial direction is 24 mm, and the farthest and nearest points in the axial direction are 190 mm and 229 mm, respectively.

Figure 9. The hysteretic characteristic of the soft module. Appl. Sci. 2018, 8, x FOR PEER REVIEW 7 of 14

Appl. Sci. 2018, 8, x FOR PEER REVIEW 7 of 14

Figure 7. The Experiment platform: (a) Experiment platform; (b) control system for the MeSMR.

50 Desired values Actual values 40

0 0 50 100 150 200 250 Step Appl. Sci. 2019, 9, 2260Figure 7. The Experiment platform: (a) Experiment platform; (b) control system for the MeSMR. 7 of 13 Figure 8. Comparison of the desired and actual values of the control system.

The soft module showed50 a well repeatable performance as shown in Figure 9. In these Desired values experiments, the soft module wasActual inflated values from 0 kPa to 60 kPa, and then deflated to 0 kPa. The results, which are shown in the40 same color, are obtained in a one-time experiment. Meanwhile, the

experiments were repeated three30 times with the same conditions. The hysteretic characteristic is also presented in Figure 9. The bending angles in the process of decreasing pressure are larger than those in the process of increasing pressure,20 which is caused by properties of the silica gel. Meanwhile, the

largest hysteretic angle is 5.5 degrees.10 Figure 10 illustrates the 2D workspace of the soft module, which is the longitudinal section of 0 its 3D workspace, forming an area0 enclosed 50 by 100 two curves. 150 200 The edges 250 of the 3D workspace of the soft module are two surfaces shown in Figure 11. TheSte maximump displacement in the radial direction is 24 mm, and the farthest and nearest points in the axial direction are 190 mm and 229 mm, respectively. FigureFigure 8. Comparison 8. Comparison of theof the desired desired and and actualactual values values of ofthe the control control system. system.

Increasing pressure The soft module showed a Decreasingwell repeatable pressure performance as shown in Figure 9. In these experiments, the soft module was inflated from 0 kPa to 60 kPa, and then deflated to 0 kPa. The results, which are shown in the same color, are obtained in a one-time experiment. Meanwhile, the experiments were repeated three times with the same conditions. The hysteretic characteristic is also presented in Figure 9. The bending angles in the process of decreasing pressure are larger than those in the process of increasing pressure, which is caused by properties of the silica gel. Meanwhile, the Appl. Sci. 2018, 8, x FOR PEER REVIEW 8 of 14 largest hysteretic angle is 5.5 degrees. Figure 10 illustrates the 2D workspace of the soft module, which is the longitudinal section of its 3D workspace, forming an area enclosed by two curves. The edges of the 3D workspace of the soft moduleAppl. Sci. 2018are , two8, x FOR surfaces PEER REVIEW shown in Figure 11. The maximum displacement in the radial direction8 of is14 Figure 9. The hysteretic characteristic of the soft module. 24 mm, and the farthestFigure and 9. nearestThe hysteretic points in the characteristic axial direction of the are soft190 module.mm and 229 mm, respectively.

Increasing pressure Decreasing pressure

Figure 10. 2D workspace of the soft module. FigureFigure Figure9. The 10. hysteretic10.2D 2D workspace workspace characteristic ofof thethe ofsoft soft the module. module.soft module.

Figure 11. 3D workspace of the soft module: (a) The nearest surface of workspace; (b) the farthest Figure 11. 3D workspace of the soft module: (a) The nearest surface of workspace; (b) the farthest Figure 11.surface 3D ofworkspace workspace. of the soft module: (a) The nearest surface of workspace; (b) the farthest surface of workspace. surface of workspace. 3.2. Experimental Model between Deformation and Pressure of the Module 3.2. Experimental Model between Deformation and Pressure of the Module 3.2. ExperimentalThe response Model betweencharacterizations Deformation of the and soft Pressuremodule are of influencedthe Module by the chamber’s structure and Thematerial’s response properties. characterizations Thus, the of experimental the soft module relationship are influenced model was by established the chamber’s based structureon and material’sTheexperimental response properties. characterizations values. As Thus, shown the in of Figure experimentalthe soft 12, moduthe fixedle relationship arecoordinate influenced system model by O-xyz the was chamber’s is established built on point structure based O, and on experimentalmaterial’swhich properties. values.is the center As Thus, point shown onthe inone Figure experimentalend of the12, module. the fixedrelationship Meanwhile, coordinate themodel posture system was sensor O-xyz established (JY61-MPU6050, is built on based point O,on whichexperimental isTelesky, the center values. Shenzhen, point As China) onshown one is endinfixed Figure of on the the 12, module.other the end fixed of Meanwhile, the coordinate module to the measuresystem posture theO-xyz sensor bending is (JY61-MPU6050,built angle. on The point O, chambers are numbered Chamber 1, Chamber 2 and Chamber 3 respectively. Telesky,which is Shenzhen, the center China) point on is fixedone end on theof the other module. end of Meanwhile, the module the to measure posture thesensor bending (JY61-MPU6050, angle. The chambersTelesky, Shenzhen, are numbered China) Chamber is fixed 1, on Chamber the other 2 end and of Chamber the module 3 respectively. to measure the bending angle. The chambers are numbered Chamber 1, Chamber 2 and Chamber 3 respectively.

Figure 12. Coordinate system and deformation analysis of the soft module: (a) Coordinate system; (b) deformation analysis.

The experiments were repeated three times with the same module parameters, and the experimental model are shown in Figure 13. The points with different shapes and colors represent Figurethe experimental12. Coordinate values, system which and were deformation obtained byanalysis inflating of theair pressure soft module: into Chamber (a) Coordinate 1. The module’ssystem; ( b) deformationbending states analysis. vary with the pressure every 10 kPa (from 0 kPa to 60 kPa). The dash lines are the

The experiments were repeated three times with the same module parameters, and the experimental model are shown in Figure 13. The points with different shapes and colors represent the experimental values, which were obtained by inflating air pressure into Chamber 1. The module’s bending states vary with the pressure every 10 kPa (from 0 kPa to 60 kPa). The dash lines are the Appl. Sci. 2018, 8, x FOR PEER REVIEW 8 of 14

Figure 10. 2D workspace of the soft module.

Figure 11. 3D workspace of the soft module: (a) The nearest surface of workspace; (b) the farthest surface of workspace.

3.2. Experimental Model between Deformation and Pressure of the Module The response characterizations of the soft module are influenced by the chamber’s structure and material’s properties. Thus, the experimental relationship model was established based on experimental values. As shown in Figure 12, the fixed coordinate system O-xyz is built on point O, Appl. Sci.which2019, 9 ,is 2260 the center point on one end of the module. Meanwhile, the posture sensor (JY61-MPU6050, 8 of 13 Telesky, Shenzhen, China) is fixed on the other end of the module to measure the bending angle. The chambers are numbered Chamber 1, Chamber 2 and Chamber 3 respectively.

Figure 12.FigureCoordinate 12. Coordinate system system and and deformation deformation analysis analysis of the of soft the module: soft module: (a) Coordinate (a) Coordinate system; (b) system; (b) deformationdeformation analysis. analysis.

The experimentsThe experiments were repeatedwere repeated three timesthree withtimes the with same the module same module parameters, parameters, and the and experimental the model areexperimental shown in model Figure are 13 shown. The in points Figure with 13. The diff erentpoints shapeswith different and colors shapes represent and colors the represent experimental the experimental values, which were obtained by inflating air pressure into Chamber 1. The module’s values, which were obtained by inflating air pressure into Chamber 1. The module’s bending states bending states vary with the pressure every 10 kPa (from 0 kPa to 60 kPa). The dash lines are the vary with the pressure every 10 kPa (from 0 kPa to 60 kPa). The dash lines are the fitting curves Appl. Sci. 2018, 8, x FOR PEER REVIEW 9 of 14 of the experimentalAppl. Sci. 2018, 8, values.x FOR PEERThe REVIEW solid line is the experimental model, which is the average9 of 14 values of three fittingfitting curves.curves of Thethe experimental results showed values. that The thesolid bending line is the angle experimental increases model, with which pressure is the and the fitting curves of the experimental values. The solid line is the experimental model, which is the curvature ofaverage curves values is a of substantial three fitting increase curves. The when result pressures showed valuesthat thewere bending over angle 30 kPa.increases Figure with 14 shows averagepressure values and the of curvature three fitting of curves curves. is aThe substantial results showedincrease whenthat the pressure bending values angle were increases over 30 with kPa. the comparison results between the simulation and experimental model. The trend of the experimental pressureFigure 14 and shows the curvaturethe comparison of curves results is a substantialbetween th increasee simulation when and pressure experimental values were model. over The 30 trend kPa. results wasFigureof basicallythe experimental14 shows in the accord resultscomparison with was basi theresultscally trend between in accord of simulation th withe simulation the trend values. and of simulation experimental Moreover, values. model. the Moreover, maximum The trend the error of the resultsofmaximum between the experimental error the of simulation theresults results was between andbasically experiments the in accordsimulati withon was and the less experimentstrend than of simulation 9 degrees.was less values. than 9Moreover, degrees. the maximum error of the results between the simulation and experiments was less than 9 degrees. Data Fit Data Fit

Average Average

Figure 13. The experimental model of chamber 1. FigureFigure 13. 13.The The experimental experimental model model of chamber of chamber 1. 1.

Simulaiton data SimulaitonExperiment data data Experiment data

Figure 14.FigureThe comparison14. The comparison results results between between the the simulation simulation and and experimental experimental modelmodel of Chamber of Chamber 1. 1. Figure 14. The comparison results between the simulation and experimental model of Chamber 1. The bendingThe bending angles ofangles the of module the module by inflatingby inflating air air pressure pressure into into Chamber Chamber 2 are 2 shown are shown in Figure in Figure 15. 15. TheThe module bending bending angles ofstates the modulevary with by the inflating pressure air every pressure 10 kPa into (from Chamber 0 kPa 2 to are 60 shown kPa). The in Figure points The module bending states vary with the pressure every 10 kPa (from 0 kPa to 60 kPa). The points 15.with The the module same shape bending represent states vary experimental with the pressure values under every different 10 kPa (from pressures. 0 kPa Theto 60 experiments kPa). The points were with the samewithrepeated the shape same three representshape times represent with experimental the experimentalsame conditions. values values As undershown different in di Figurefferent pressures. 15, pressures. the dash The lines experiments The are experimentsthe fitting were were repeated threerepeatedcurves times of three the with experimentaltimes thewith same the values; same conditions. conditions.the solid line AsAs representsshown shown in inFigure the Figure experimental 15, the 15 dash, the model, lines dash are which lines the fitting is are the the fitting curves of the experimental values; the solid line represents the experimental model, which is the curves of theaverage experimental values of the values;fitting curv thees. solidThe results line were represents three-dimensional the experimental curves because model, the rotate which is the averageaxis was values not coincident of the fitting to thecurv axises. Theof the results fixed were coordinate three-dimensional system. Chambers curves because 2 and 3the are rotate axial average values of the fitting curves. The results were three-dimensional curves because the rotate axis axissymmetry was not about coincident x-axis. Thus,to the the axis bending of the angle fixed of coordinate Chamber 3system. can be Chambersobtained by 2 theand experimental 3 are axial was not coincidentsymmetrymodel of Chamber about to the x-axis. 2. axis Figure Thus, of the16 the illustratesfixed bending coordinate the angle comp of arisonChamber system. of the 3 can simulation Chambers be obtained and 2 byexperimental and the 3experimental are axial model. symmetry modelThe maximum of Chamber error 2. ofFigure the results 16 illustrates between the the comp simulatiarisonon of and the experiment simulation andis less experimental than 10 degrees. model. The maximumAs shown error in Figure of the results14 and between Figure 16,the theresimulati wereon andsome experiment deviations is lessof the than simulations 10 degrees. and experimentalAs shown results in Figure because 14 andof the Figure idealization 16, there errors. were Particularly,some deviations the main of the factor simulations incurring and the experimentaldeviation is the results thickness because uniformity of the idealizationof the soft modu errors.le, whichParticularly, cannot bethe entirely main factor consistent incurring with thatthe deviationin the simulation. is the thickness uniformity of the soft module, which cannot be entirely consistent with that in the simulation. Appl. Sci. 2019, 9, 2260 9 of 13 about x-axis. Thus, the bending angle of Chamber 3 can be obtained by the experimental model of ChamberAppl. 2. FigureSci. 2018, 8 ,16 x FOR illustrates PEER REVIEW the comparison of the simulation and experimental10 of model. 14 The

maximumAppl. error Sci. 2018 of the, 8, x FOR results PEER REVIEW between the simulation and experiment is less than 10 degrees.10 of 14

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(

e r

u Data Fit

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Appl. Sci. 2018, 8, x FOR PEER REVIEWr 10 of 14

Average

Figure 15. The experimental model of chamber 2. FigureFigure 15. 15.The The experimental experimental model model of chamber of chamber 2. 2.

Simulaiton data Figure 15. The experimentalExperiment data model of chamber 2. Simulaiton data Experiment data

Figure 16.FigureThe comparison16. The comparison results results between between the the simulation and and experimental experimental model modelof Chamber of Chamber2. 2. Simulaiton data Figure 16. The comparison results between the simulation and experimental model of Chamber 2. As shown4. Experiments in Figures and 14Results and 16, thereExperiment were data some deviations of the simulations and experimental results because4. Experiments of the idealizationand Results errors. Particularly, the main factor incurring the deviation is the 4.1. Bending Motion thickness uniformity of the soft module, which cannot be entirely consistent with that in the simulation. 4.1. BendingFigure 1Motion7 presents the module in which air is pumped at a pressure ranging from 0 kPa to 40

4. ExperimentskPa, intoFigure and two 17 Resultschambers presents tothe realize module 3- Din motion. which airFigure is pumped 18 shows at athe pressure flow chart ranging of controlling from 0 kPa process to 40 forkPa, theFigure into soft two 16. model The chambers comparison. The results to realize results illustrate 3-Dbetween motion. the the effectiveness simulati Figureon 18 and shows of experimental the thecontrol flow modelsystem chart of of andChamber controlling the reliability 2. process of thefor thegas softsupply model. connecting The results part, illustrate which can the supplyeffectiveness air pressure of the controlto all chambers system and of thethe softreliability module of 4.1. Bending4. Experiments Motion and Results usingthe gas only supply one airconnecting tube. part, which can supply air pressure to all chambers of the soft module using only one air tube. Figure4.1. Bending17 presents Motion the module in which air is pumped at a pressure ranging from 0 kPa to 40 kPa, into two chambers to realize 3-D motion. Figure 18 shows the ﬂow chart of controlling process for the Figure 17 presents the module in which air is pumped at a pressure ranging from 0 kPa to 40 soft model.kPa, Theinto two results chambers illustrate to realize the 3-D e ﬀmotion.ectiveness Figure of18 shows the control the flow system chart of controlling and the reliability process of the gas supply connectingfor the soft model. part, The which results can illustrate supply the aireffectiveness pressure ofto the all control chambers system and of the the softreliability module of using only one air tube.the gas supply connecting part, which can supply air pressure to all chambers of the soft module using only one air tube.

Figure 17. Three-dimensional motion of the module: (a) Cross section of module; (b–i) a series of framesFigure captured17. Three-dimensional from video for motion three-dimensional of the module: motion (a) .Cross section of module; (b–i) a series of frames captured from video for three-dimensional motion.

Figure 17.FigureThree-dimensional 17. Three-dimensional motion motion of of the the module: module: (a) Cross (a) Crosssection of section module; of (b module;–i) a series (ofb– i) a series of frames capturedframes captured from from video video for for three-dimensional three-dimensional motion. motion. Appl. Sci. 2018, 8, x FOR PEER REVIEW 11 of 14

Appl. Sci. 2019, 9, 2260 10 of 13 Appl. Sci. 2018, 8, x FOR PEER REVIEW 11 of 14

Figure 18. The flow chart of three-dimensional motion.

4.2. Hooking Object The soft robot consistsFigure of Module 18. The A ﬂow and chart Module of three-dimensionalB, as shown in Figure motion. 19. The chambers of the Figure 18. The flow chart of three-dimensional motion. module are numbered Yi (i = 1, 2, 3), where Y is the number of modules and i is the number of modules 4.2. HookingY’s chamber. Object Therefore, A1, A2 and A3 are expressed in three chambers of Module A, as shown in 4.2. Hooking Object Figure 19. The softFigureThe robot soft 19 robotalso consists shows consists ofthe Moduleof process Module Aof A andhooking and ModuleModule a ho llowB, B, as as objectshown shown using in Figure in a Figure soft 19. robot. The 19. chambers TheThe robot chambers of is the of the moduleinflatedmodule are numbered with are numberedair to Yformi (i Y= ian (1,i =anchor-like 2,1, 2, 3), 3), where where shapeY Y isis unthe til number number 30 kPa. of Accordingly, ofmodules modules and andthei is theroboti is number the can number pick of modules up ofa modules Y’s chamber.hollowY’s chamber. object. Therefore, Moreover, Therefore, A1, the A1, A2 sharing A2 and and A3air A3 pressure are are expressedexpresse functidon in inof three threethe gaschambers chambers controller of Module part of Moduleof the A, moduleas shown A, as isshown in in Figureverified. Figure19. 19.In future work, we will develop a carrying capacity for the module to realize robots picking up heavyFigure objects. 19 also shows the process of hooking a hollow object using a soft robot. The robot is inflated with air to form an anchor-like shape until 30 kPa. Accordingly, the robot can pick up a hollow object. Moreover, the sharing air pressure function of the gas controller part of the module is verified. In future work, we will develop a carrying capacity for the module to realize robots picking up heavy objects.

FigureFigure 19. Hooking 19. Hooking object object of theof the soft soft robot: robot: ( a(a)) Cross Cross sectionsection of of hooking hooking configuration conﬁguration robot; robot; (b–f ()b a– f) a series of framesseriescaptured of frames captured from video from for video hooking for hooking object. object.

Figure4.3. Twisting 19 also Motion shows the process of hooking a hollow object using a soft robot. The robot is inflated with air toThe formFigure crossing an19. anchor-likeHooking configuration object shape of robotthe soft until consists robot: 30 ( kPa.aof) Crossfour Accordingly, modules,section of hookingand the the robotconfigurationidentifiers can pickof robot; modules up (b a–f) hollow anda object. series of frames captured from video for hooking object. Moreover,chambers the sharingare shown air in pressure Figure 20, function which also of illustrates the gas controller that the robot part has of a the grasping module ability is verified. and can In future twist the object. The robot is actuated, i.e., all modules’ Chamber 1 are inflated with air until 40 kPa, work, we4.3. willTwisting develop Motion a carrying capacity for the module to realize robots picking up heavy objects. to form the shape of a claw for holding the object, as shown in Figure 20c,d. Then, the air is pumped 4.3. Twistinginto ChamberThe Motion crossing 2 of each configuration module to robotproduce consists a torsiona of folur force. modules, Therefore, and the the identifiersobject can ofbe modulestwisted by and thechambers crossing are configuration shown in Figure robot, 20,as shownwhich alsoin Figure illustrates 20e. that the robot has a grasping ability and can Thetwist crossingAs the the object. experimental configuration The robot result is actuated, robot shows, consists i.e.,four all module modu of fourles’s share Chamber modules, air pressure 1 are and inflated with the identifiersone with air air supply until of 40 modulesand kPa, and chambersaccomplishto form are the shown a shapetwisting in of Figure motion.a claw for20 Therefore, ,holding which the also probleobject, illustratesm as of shown too thatmany in theFigure tubes, robot 20c,d. which has Then, incurs a grasping the drag air andis abilitypumped pull, and can is effectively solved. twist theinto object. Chamber The 2 robotof each is module actuated, to produce i.e., all a modules’ torsional force. Chamber Therefore, 1 are the inflated object withcan be air twisted until by 40 kPa, to the crossing configuration robot, as shown in Figure 20e. form the shape of a claw for holding the object, as shown in Figure 20c,d. Then, the air is pumped into As the experimental result shows, four modules share air pressure with one air supply and Chamberaccomplish 2 of each a twisting module motion. to produce Therefore, a torsional the problem force. of too Therefore, many tubes, the which object incurs can drag be and twisted pull, by the crossingisAppl. effectively configuration Sci. 2018, 8solved., x FOR robot, PEER REVIEW as shown in Figure 20e. 12 of 14

FigureFigure 20. Twisting 20. Twisting motion motion of of the the softsoft robot: robot: (a) ( aCross) Cross section section of the ofcrossing the crossing configuration conﬁguration robot; (b– robot; (b–f) a seriesf) a series of of frames frames captured captured from from video video for for twisting twisting motion. motion.

4.4. Bionic gesture As shown in Figure 21, the soft robot is composed of five soft modules, and each module is similar to the finger of the human hand. The robot can form three types of gestures, namely, OK, Yeah, and Love. The pressure values of the different gestures are shown in Table 2.

Figure 21. Bionic gestures of the soft robot: (a) cross section of fingers; (b) initial state of fingers; (c) OK gesture; (d) Yeah gesture; (e) Love gesture.

Table 2. Pressure values of the different gestures.

Gesture Chambers/Values (kPa) OK A1/45 B3/45 B1/30 —— Yeah A1/40 A3/40 D1/45 E1/45 Love C1/45 D1/45 —— ——

In Table 2 and Figure 20, the soft robot forms the OK gesture when the pressure values of A1, B1, and B3 are 45 kPa, 30 kPa and 45 kPa, respectively. When the figure forms the Yeah gesture, the values are 40 kPa for A1 and A3 and 45 kPa for D1 and E1. The Love gesture can also be formed by inflating the air pressure of C1 and D1 to 45 kPa. Therefore, the soft robot can form various gestures by inflating different values of air pressure into the modules’ chambers. Moreover, the gas controller can supply air for five modules at the same time. This method provides an effective approach for researchers to design soft modular robots.

5. Conclusions In this paper, we designed a mechatronics-embedded pneumatic soft module with a novel structure and introduced the manufacturing process. The soft module is made from silica gel, which has a continuous deformation characteristic. Moreover, the hysteretic characteristic and 3D workspace of the soft module are analyzed. In order to obtain the relationship between the deformation and pressure of the module, the experimental models are established and compared with simulations. Furthermore, we perform four types of experiments of the MeSMR, including Appl. Sci. 2018, 8, x FOR PEER REVIEW 12 of 14

Appl. Sci. 2019, 9, 2260 11 of 13

As the experimental result shows, four modules share air pressure with one air supply and accomplish a twisting motion. Therefore, the problem of too many tubes, which incurs drag and pull, is effectively solved. Figure 20. Twisting motion of the soft robot: (a) Cross section of the crossing configuration robot; (b– 4.4. Bionic gesturef) a series of frames captured from video for twisting motion. As4.4. shown Bionic in gesture Figure 21, the soft robot is composed of five soft modules, and each module is similar to the fingerAs of shown the human in Figure hand. 21, the The soft robot robot can is compos form threeed of typesfive soft of modules, gestures, and namely, each module OK, Yeah, is and Love. Thesimilar pressure to the finger values of of the the human different hand. gestures The robo aret can shown form three in Table types2 .of gestures, namely, OK, Yeah, and Love. The pressure values of the different gestures are shown in Table 2.

Figure 21.FigureBionic 21. Bionic gestures gestures of the of softthe soft robot: robot: (a )(a cross) cross section section ofof ﬁngers;fingers; (b (b) )initial initial state state of fingers; of ﬁngers; (c) (c) OK gesture;OK (d )gesture; Yeah gesture; (d) Yeah (gesture;e) Love (e gesture.) Love gesture.

TableTable 2. Pressure2. Pressure values values of the the different diﬀerent gestures. gestures.

GestureGesture Chambers/Values Chambers/Values (kPa) (kPa) OK A1/45 B3/45 B1/30 —— OK A1/45 B3/45 B1/30 — — Yeah A1/40 A3/40 D1/45 E1/45 YeahLove A1/40 C1/45 A3D1/45/40 —— D1/——45 E1/45 Love C1/45 D1/45 — — — — In Table 2 and Figure 20, the soft robot forms the OK gesture when the pressure values of A1, B1, and B3 are 45 kPa, 30 kPa and 45 kPa, respectively. When the figure forms the Yeah gesture, the Invalues Table2 are and 40 FigurekPa for 20A1, and the A3 soft and robot 45 kPa forms for D1 the and OK E1. gesture The Love when gesture the can pressure also be valuesformed ofby A1, B1, and B3 areinflating 45 kPa, the air 30 pressure kPa and of 45 C1 kPa, and respectively.D1 to 45 kPa. Therefore, When the the figure soft robot forms can the form Yeah various gesture, gestures the values are 40 kPaby inflating for A1 anddifferent A3 andvalues 45 of kPa air pressure for D1 andinto the E1. modules’ The Love chambers. gesture Moreover, can also the be formedgas controller by inflating can supply air for five modules at the same time. This method provides an effective approach for the air pressure of C1 and D1 to 45 kPa. Therefore, the soft robot can form various gestures by inflating researchers to design soft modular robots. different values of air pressure into the modules’ chambers. Moreover, the gas controller can supply air for five5. Conclusions modules at the same time. This method provides an effective approach for researchers to design soft modularIn this paper, robots. we designed a mechatronics-embedded pneumatic soft module with a novel structure and introduced the manufacturing process. The soft module is made from silica gel, which 5. Conclusionshas a continuous deformation characteristic. Moreover, the hysteretic characteristic and 3D In thisworkspace paper, of we the designed soft module a mechatronics-embedded are analyzed. In order pneumaticto obtain the soft relationship module with between a novel the structure deformation and pressure of the module, the experimental models are established and compared and introducedwith simulations. the manufacturing Furthermore, we process. perform Thefour softtypes module of experi isments made of fromthe MeSMR, silica gel,including which has a continuous deformation characteristic. Moreover, the hysteretic characteristic and 3D workspace of the soft module are analyzed. In order to obtain the relationship between the deformation and pressure of the module, the experimental models are established and compared with simulations. Furthermore, we perform four types of experiments of the MeSMR, including bending motion, hooking object, twisting motion and bionic gesture, on a wireless control platform. The results illustrate the reliability of the mechatronics-embedded module and the effectiveness of the air pressure sharing function. The MeSMR proposed in this paper can realize air sharing functions via a signal air tube, which effectively solves the dragging and intertwine problems of excessive air tubes, although the MeSMR has the limitation of parallel inflation and deflation of the actuators. The achieved results can provide the references for researchers to design soft modular robots in the future. Based on the current experimental Appl. Sci. 2019, 9, 2260 12 of 13 results, we will continue to research the motions of the MeSMR with different configurations. Such work will enhance the adaptability of soft modular robots to various tasks.

Author Contributions: Conceptualization, Y.Z. (Yu Zhang), Y.Z. (Yanhe Zhu) and J.Z.; methodology, Y.Z. (Yu Zhang), Y.L. and T.Z.; validation, Y.Z. (Yu zhang), Y.L. and X.S.; data curation, X.S., D.B. and Y.W.; writing—original draft preparation, Y.Z. (Yu Zhang); all authors have read and approved the final manuscript. Funding: This work was supported in part by the Joint Research Fund (U1713201) between the National Natural Science Foundation of China (NSFC) and Shen Zhen and in part by the NSFC under Grant 61673137. Conflicts of Interest: The authors declare no conflict of interest.

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