Soft Robotics: a Review of Recent Developments of Pneumatic Soft Actuators

actuators

Review Soft Robotics: A Review of Recent Developments of Pneumatic Soft Actuators

James Walker 1, Thomas Zidek 1, Cory Harbel 1, Sanghyun Yoon 1, F. Sterling Strickland 2, Srinivas Kumar 1 and Minchul Shin 1,*

1 Department of Mechanical Engineering, Georgia Southern University, Statesboro, GA 30460, USA; [email protected] (J.W.); [email protected] (T.Z.); [email protected] (C.H.); [email protected] (S.Y.); [email protected] (S.K.) 2 Department of Manufacturing Engineering at Georgia Southern University, Statesboro, GA 30460, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-912-478-5759

Received: 29 September 2019; Accepted: 11 December 2019; Published: 10 January 2020

Abstract: This paper focuses on the recent development of soft pneumatic actuators for soft robotics over the past few years, concentrating on the following four categories: control systems, material and construction, modeling, and sensors. This review work seeks to provide an accelerated entrance to new researchers in the field to encourage research and innovation. Advances in methods to accurately model soft robotic actuators have been researched, optimizing and making numerous soft robotic designs applicable to medical, manufacturing, and electronics applications. Multi-material 3D printed and fiber optic soft pneumatic actuators have been developed, which will allow for more accurate positioning and tactile feedback for soft robotic systems. Also, a variety of research teams have made improvements to soft robot control systems to utilize soft pneumatic actuators to allow for operations to move more effectively. This review work provides an accessible repository of recent information and comparisons between similar works. Future issues facing soft robotic actuators include portable and flexible power supplies, circuit boards, and drive components.

Keywords: soft robotics; soft pneumatic actuators; control systems; sensors; soft materials; modeling

1. Introduction Robots are becoming ubiquitous in modern society. Robots have changed the way humanity manufactures our goods, performs surgery, and transport ourselves along with our possessions. However, the boundary between robots and humans remains hazardous [1] as the modern day hard robot requires a lot of precise control and while they excel at repetitive massive production functions, they struggle to interact with uncertain environments [2]. This is where the new field of soft robotics excels. A soft robot is primarily composed of stretchable, flexible materials, such as silicon rubber. While these softer materials have a lower rigidity when compared to metals used in hard robots, these softer robots have provided more flexibility and adaptability to the workspace creating safer environments, particularly in a human-robot integrated workspace. There have been many exciting developments and research around soft robotics recently, which will be chronicled in this review study. The most important physical aspect of a robot is movement [3]. For soft robots, movement is conducted by using variable length tendons [3], by pneumatic actuations, or even more recently by twist-and-coil actuators [4], as seen in Figure1. Many of the modern actuation methods have been designed to replicate biological movements, such as fish fins or octopus tentacles [5–10].

Actuators 2020, 9, 3; doi:10.3390/act9010003 www.mdpi.com/journal/actuators Actuators 2020, 9, 3 2 of 26 Actuators 2020, 9, 3 2 of 27

Figure 1. 1. VariousVarious soft soft robots robots with with various various actuation actuation methods methods (a) Actuator (a) Actuator with multi-material with multi-material 3D printing 3D printing[11]. (Open [11 Access);]. (Open (b Access);) Soft linear (b) Softactuator linear [12]. actuator (Open [Access);12]. (Open (c) Design Access); for (c )bidirectional Design for bidirectionalbending [13]. bending(Copyright [13 2015,]. (Copyright Elsevier); 2015, (d) Snake-modeled Elsevier); (d) Snake-modeled robot [14] (Copyright robot [14 2012,] (Copyright Taylor & 2012, Francis). Taylor & Francis).

Many soft robots use pneumatics or hydraulics to actuate their bodies, but another way actuation has beenbeen implementedimplemented into soft robots isis throughthrough thethe developmentdevelopment ofof electroactiveelectroactive polymerspolymers whichwhich electrically activate soft actuators in order to cause movement [[15–18].15–18]. The flexibleflexible nature of softsoft robotsrobots necessitates the the development development of of stretchable stretchable electronics electronics such such as sensors as sensors and andpower power sources. sources. The Thecompliance compliance and morphology and morphology of soft of robots soft robots prevent prevent the use the of usemany of conventional many conventional sensors sensors seen in seenhard inrobots, hard so robots, there sohas there been has active been research active research into stretchable into stretchable electronic electronic sensors and sensors curvature and curvature sensors. sensors.Elastomer Elastomer sensors allow sensors for allow minima forl minimal impact to impact the actuation to the actuation of the robot of the [19]. robot Along [19]. Along with these with thesesensors, sensors, soft robots soft robots also alsohave have the thecapability capability of ofhosting hosting chemical chemical and and biological biological sensors sensors to sensesense environmental signals which could eventually allow them to be human-centeredhuman-centered tools for the idealideal workspaceworkspace between humans and robotsrobots [[5].5]. Power sources have also been a bigbig pointpoint ofof emphasisemphasis withwith generalgeneral compressors compressors using using a lota lot of electricity,of electricity, alternative alternative methods methods such such as chemical as chemical reaction-based reaction- sourcesbased sources and combustible and combustible fuels have fuels been have looked been atlooked for solutions at for solutions [6,20,21]. [6,20,21]. In turn, manyIn turn, soft many robotics soft designsrobotics havedesigns been have made been using made ‘2.5D’ using designs, ‘2.5D’ limited designs, primarily limited byprimarily 3D printed by 3D capabilities printed capabilities and model complexity,and model orcomplexity, use abstract or approachesuse abstract with approaches the current with software the current available software [22–24 ].available Fabrication [22–24]. has alsoFabrication been an has area also of interest;been an however,area of interest; thanks howe to rapidver, growth thanks into digitalrapid growth design andin digital fabrication design tools, and therefabrication are now tools, many there ways are now to develop many ways soft robotsto develo withp soft diff erentrobots material with different selections material and asselections well as embeddedand as well needed as embedded electronics needed into electronics channels of into the ch materialannels of for the system material use [for11 ,system25–28]. use [11,25–28]. The movements of of soft soft bodies bodies can can be be described described by by an an infinite infinite number number of ofdegrees degrees of freedom. of freedom. Its Itsintrinsic intrinsic deformation deformation such such as asbend, bend, twist, twist, stretch, stretch, compress, compress, buckle, buckle, wrinkle, wrinkle, etc. etc. makes makes the the control of soft robots very challenging [[7].7]. The muscle analogyanalogy of soft organisms is introduced to understand thethe workingworking principle principle of softof soft bodies. bodies. An understandingAn understanding of the principleof the principle of octopus of hasoctopus led to inspirationhas led to forinspiration modeling for and modeling control and [7]. control [7]. Due to itsits absentabsent ofof rigidrigid structure,structure, thethe kinematicskinematics andand dynamicsdynamics ofof soft-roboticsoft-robotic systemssystems are didifferentfferent fromfrom thatthat ofof conventionalconventional systems.systems. Therefore,Therefore, researchers have developed a new static, dynamic, andand kinematic models for soft-robotic system.system. Designers modelmodel thethe kinematics of soft robots using piecewisepiecewise constant curvature (PCC) [[8].8]. In approaches of developingdeveloping methodsmethods toto connectconnect thethe actuation space to the configurationconfiguration space,space, the morphologymorphology of the robot bodybody andand thethe characteristicscharacteristics of the actuation system is studied and and combined. combined. Some Some forward forward kinematics kinematics basis basis approaches approaches are are a ause use of of Bernoulli-Euler Bernoulli-Euler beam beam mechanics mechanics to to predict predict deformation deformation [8], [8], a a relationship between the jointjoint variablesvariables andand thethe curvature arcarc parametersparameters ofof highhigh andand mediummedium pressurepressure robot.robot. Existing solutions of the inverse-kinematics problem are neglecting the consideration of the pose of the end effector [9,29]. This limited computation creates difficulty in avoidance of autonomous obstacles and movement

Actuators 2020, 9, 3 3 of 26 the inverse-kinematics problem are neglecting the consideration of the pose of the end effector [9,29]. This limited computation creates difficulty in avoidance of autonomous obstacles and movement under restrained environment. Without consideration of the entire robot body including the end effector, task-space planning algorithms which allows a detailed tasks, such as maneuvers in confined circumstances and grasping or placing objects, are challenging [30]. When the robot is used in the environment, the use of compliance is key to the developed algorithms; using compliance, the robot can interact with objects with unknown geometry [4,5,31]. Soft robots are controlled in low-level using pressure transducers which provides differences in actuator compliance, or volume control using strain sensors. Volume control is very effective in configuration control and assists setting a maximum safe displacement [1,4,7,12,32]. Valve sequencing control is controlling the body-segment actuator by pressurizing the actuator for a period by turning the valve on and off [10]. Recent work has tried to develop more compact control elements for pneumatic soft robots that does not require electrical control signals. Without electrical control, the addressable control of actuators from a single pressure source is possible [33]. Applications for soft robotics include locomotion, manipulation, medical devices, and wearable applications [34]. Many of the locomotion techniques have been modeled after fluid moving creatures such as caterpillars [35], worms, octopus, and fish. Many of these, like the octopus, provide a model for morphological computations [36]. Most of locomotion has been enhanced by using electrical tethers, friction manipulation and motor-tendon actuation [37]. Another boon of soft robotics is the natural advantage they have with object deformation. Due to the flexibility of the soft robots, they can manipulate their bodies, especially a gripper or finger [15,38], to grab objects of varying size and shape. Since the material of soft robotics is modeled after biological systems, soft robotics make for a very good option in the field of medical and wearable devices due to their flexibility and absorption factors, such as seen in recent reverse pneumatic artificial muscle technology [12,13,39] and manipulators for minimally invasive surgery [40]. The recent advances of note in the field of soft robotics can be broken into four categories, control systems, materials and construction, modeling, and sensors, which will be explored in the following pages. These four subjects have been identified as pertaining most directly with the widespread adaptation of soft robotics both industrially and commercially, as they begin to solve the problems soft robotics have with repeatability, manufacturability, and control stability. Therefore, the recent research selected for this article have been chosen due to their contribution to solutions to the problems preventing the widespread application of soft robotics. Soft robotics could be deployed medically to improve patient care, domestically to provide safe and useful household robots, and industrially to provide flexible robots compatible with human coworkers [1,2,34,40]. Mobile soft robots could also provide search and rescue operations with scouting and extraction robots capable of moving through uneven and uncertain terrain without complex control algorithms because of their inherent compliance [4,5,31,41]. The field of soft robotics is so complex that a review of the field is necessary to provide newcomers an expedient entry to the research. Even for those familiar with the field, the amount of new research being generated makes it difficult to stay abreast of current events. This paper aims to provide a good entry point to the complex subject of soft robotics, specifically for recent developments in the four aforementioned topics, such that others may be able to use this review as a springboard for their independent research efforts. This review may also serve as a tool for experienced researchers to quickly identify interesting research related to their own projects. This paper also identifies some areas of soft robotics that have not received as much research, and therefore present opportunities for new meaningful research. Sources for this paper were primarily found using the Georgia Southern University Library resources. Several rounds of search were conducted, first for non-specific soft robotics papers to establish background knowledge and an understanding of the history of the field. Key words included “soft robotics,” “design,” “fabrication,” “soft material,” “soft sensors,” “flexible robots,” and others. Actuators 2020, 9, 3 4 of 26

Actuators 2020, 9, 3 4 of 27 It was important that these papers be published prior to 2016 to properly identify the foundational for thetechnologies general search and traditional as well systemsas some of keywords soft robotics. more After specific the establishment to each topic. of foundational In order to research, ensure that theseindividual papers represent searches were recent conducted developments to identify in recent the field, and useful these papers papers and were articles restricted regarding to thepapers publishedfour topics after aforementioned. 2015. Studies Keythat words were included not sour theced previous from a keywords reputable used publisher, for the general had searchsignificant conflictsas well of asinterest, some keywords or had too more few specific citations to each were topic. not Inconsidered order to ensure for inclusion that these in papers this review represent article. recent developments in the field, these papers were restricted to papers published after 2015. Studies The bibliography is primarily restricted to peer reviewed scholarly articles and papers. that were not sourced from a reputable publisher, had significant conflicts of interest, or had too few citations were not considered for inclusion in this review article. The bibliography is primarily 2. Control Systems restricted to peer reviewed scholarly articles and papers. Control Systems over the years have evolved, and now the standard for soft robotics is using 2. Control Systems pneumatic systems to move the structure of the robot, although some hydraulic systems [42–44] are used. In Controlorder to Systems control over pneumatic the years and have hydraulic evolved, andsystems, now theeither standard an open for softor closed robotics loop is using must be implementedpneumatic [16,45–49]. systems to moveWhich the option, structure open of the or robot,closed although loop, is somebetter hydraulic depends systems on the [ 42application–44] are to be accomplishedused. In order with to control the soft pneumatic robotics and [12,37,46,50–54]. hydraulic systems, either an open or closed loop must be implementedThere have been [16,45 studies–49]. Which that option, show the open pros or closed and cons loop, to is betterboth open depends and on closed the application loops [55–57], to be but accomplished with the soft robotics [12,37,46,50–54]. the most controversial part was dependent on the presence of a feedback system paired with the There have been studies that show the pros and cons to both open and closed loops [55–57], but the loops.most In the controversial original development part was dependent of soft on robots, the presence the designs of a feedback were intended system pairedto optimize with the performance loops. and Inincrease the original effectiveness development while of soft enabling robots, thesafe designs interactions were intended with the to optimize environment; performance however, and the traditionalincrease feedback-only effectiveness while actions enabling that safe have interactions been developed with the environment; for higher however, accuracy the control traditional of soft roboticsfeedback-only do not satisfy actions all that of havethese been conditions. developed To for answer higher accuracythis issue, control an algorithm of soft robotics called do Iterative not Learningsatisfy Control all of these (ILC) conditions. was developed. To answer When this the issue, ILC an algorithm algorithm was called used Iterative with Learninga low stiffness Control robot, the robot(ILC) was developed.able to recognize When thean object ILC algorithm in the environment was used with and a lowstop sti itsffness own robot, motion the before robot was moving the objectable to recognize[58]. There an objectis also in a the control environment system and known stop its as own a Proportional-I motion before movingntegral-Derivative the object [58 ].(PID) controller,There is which also a controlis a control system loop known mechanism as a Proportional-Integral-Derivative that utilizes feedback to keep (PID) a constant controller, variable. which is Due to thea controlfact that loop the mechanism PID controller that utilizes only impacts feedback on toe keep variable a constant other variable.control methods Due to the have fact thatbeen the tested PID controller only impacts one variable other control methods have been tested [59] A PID can be [59] A PID can be part of the control system, but there needs to be more hardware that effectively part of the control system, but there needs to be more hardware that effectively communicates to the communicates to the servo motors or solenoids that activate the open or closed loops [60,61]. servo motors or solenoids that activate the open or closed loops [60,61]. UsuallyUsually an anArduino Arduino board board isis used used to driveto drive the controlthe control system system due to its due ability to toits come ability in di tofferent come in differentsizes alongsizes withalong the with fact the that fact it is easythat toit codeis easy as itto accept code manyas it accept different many languages different [62]. languages It also allows [62]. It also theallows ability the to controlability servoto control motors, servo biological motors, muscles, biological and other muscles, actuators and to other move actuators or have the to ability move or haveto the pick ability up objects, to pick as seenup objects, in Figures as2 andseen3. in The Fi Arduinogures 2 isand not 3. the The only Arduino thing that is cannot be the used only though, thing that can asbe long used as thethough, soft robotics as long has aas microcontroller the soft robotics that is ablehas toa freelymicrocontroller communicate that through is able it to ato PC freely communicateusing a system through such asit to MATLAB, a PC using Simulink, a syst etc.em [such63]. as MATLAB, Simulink, etc. [63].

Actuators 2020, 9, 3 5 of 26 Actuators 2020, 9, 3 5 of 27

UsingUsing a a microcontroller, microcontroller, for example an an Arduino or or a Raspberry Pi, Pi, there is an opportunity to to controlcontrol and and utilize utilize the the magnetic magnetic field field [15,16,18,65]. [15,16,18,65]. The The magnetic magnetic fields fields allow allow for for an an electro-adhesive electro-adhesive forceforce on on an an object object and and to to control control it it depending depending on on how how the the positive positive and and negative negative fields fields are are positioned. positioned. TheThe changechange inin the the magnetic magnetic field field causes causes the compliant the compliant electrodes electrodes to deform, to resultingdeform, inresulting a deformation in a deformationacross the actuator. across the As actuator. seen in Figure As seen2, the in Figure electric 2, field the changeselectric field due changes to the location due to ofthe the location positive of theand positive negative and fields negative are placed. fields Ifare a negativeplaced. If is a placed negative next isto placed a negative next to it repelsa negative one anotherit repels but one if anothera positive but is if placed a positive next is to placed a negative next they to a arenegative attracted they to are one attracted another. to This onecauses another. the This relationship causes the to relationshipchange more to significantly change more when significantly multiple positivewhen mult or negativeiple positive fields or are negative utilized. fields Key are points utilized. of control Key pointssystems of arecontrol summarized systems beloware summarized in Figures 4below–6. in Figures 4–6. MLFFN and RBFN controllers both utilize predefined trajectory control to predict the position of a robotic manipulator and adjust input values accordingly to stabilize and bring a robotic manipulator to a desired position [59]. From Figure5 above, The Instantaneous Mean Squared Error (MSE) was compared between a MLFFN Controller and RBFN Controller. The results showed the ability of the more complex RBFN controller’s ability to reduce error within the system much faster than the MLFFN controller [59]. However, the biggest disadvantage of the RBFN controller is its ability to control a simpler system when compared to a traditional PD system; RBFNs are more complicated than a simple

Actuators 2020, 9, 3 6 of 26 Actuators 2020, 9, 3 6 of 27 Actuators 2020, 9, 3 6 of 27 system requires [59]. When comparing the two block diagrams in Figures4 and6 between a PD

Actuatorscontroller 2020, and9, 3 the RBFN block diagram, the PD controller can adequately control its system using6 of 27 a simpler model. A comparison between diﬀerent types of control systems for soft robotics is in Table1.

Figure 4. RBFN-based control configuration for trajectory control of two-link robotic manipulator [59] Figure 4. RBFN-based control configuration for trajectory control of two-link robotic manipulator [59] (Copyright 2016, Springer Nature). Figure 4. RBFN-based control conﬁguration for trajectory control of two-link robotic manipulator [59] (CopyrightFigure 4. RBFN-based 2016, Springer control Nature). configuration for trajectory control of two-link robotic manipulator [59] (Copyright 2016, Springer Nature). (Copyright 2016, Springer Nature).

Figure 5. MSE under RBFN controller action [59] (Copyright 2016, Springer Nature). FigureFigure 5. 5.MSEMSE under under RBFN RBFN controller controller action action [[59]59] (Copyright(Copyright 2016, 2016, Springer Springer Nature). Nature). Figure 5. MSE under RBFN controller action [59] (Copyright 2016, Springer Nature).

FigureFigure 6. 6.RegularRegular proportional-derivative proportional-derivative control algorithmalgorithm diagram diagram for for ﬂow-based flow-based position position control control of FigureFigure 6.6. Regular proportional-derivativeproportional-derivative control control algori algorithmthm diagram diagram for for flow-based flow-based position position control control of the robotic bilateralbilateral [ 66[66]] (Copyright (Copyright 2013, 2013, Elsevier). Elsevier). ofof thethe roboticrobotic bilateral [66][66] (Copyright (Copyright 2013, 2013, Elsevier). Elsevier). MLFFN and RBFN controllers both utilize predefined trajectory control to predict the position MLFFNMLFFN and RBFN controllerscontrollers both both utilize utilize predefined predefined trajectory trajectory control control to predictto predict the theposition position of a robotic manipulator and adjust input values accordingly to stabilize and bring a robotic ofof aa roboticrobotic manipulator andand adjustadjust inputinput values values accordingly accordingly to tostabilize stabilize and and bring bring a robotic a robotic manipulator to a desired position [59]. From Figure 5 above, The Instantaneous Mean Squared Error manipulatormanipulator to a desired positionposition [59]. [59]. From From Figu Figurere 5 above,5 above, The The Instantaneous Instantaneous Mean Mean Squared Squared Error Error (MSE) was compared between a MLFFN Controller and RBFN Controller. The results showed the (MSE)(MSE) waswas comparedcompared betweenbetween a a MLFFN MLFFN Controller Controller and and RBFN RBFN Controller. Controller. Th eTh resultse results showed showed the the ability of the more complex RBFN controller’s ability to reduce error within the system much faster abilityability ofof thethe more complex RBFNRBFN controller’s controller’s ability ability to to reduce reduce error error within within the the system system much much faster faster than the MLFFN controller [59]. However, the biggest disadvantage of the RBFN controller is its thanthan thethe MLFFNMLFFN controller [59].[59]. However,However, the the bigg biggestest disadvantage disadvantage of ofthe the RBFN RBFN controller controller is its is its

Actuators 2020, 9, 3 7 of 26

Table 1. Comparison of diﬀerent control systems.

Driving Systems Controller Advantage Disadvantage Predeﬁned trajectory of • one-link and two-link robotic manipulators Non-linear RBFN [59] • Fixed structure complexity Highly complex system Radial basis function • • network Instantaneous error • converges to zero faster than MLFFN by approximately 1.5 s

Ability to estimate plant • Requires accurate estimation uncertainties and • Disturbance Observer for range of external disturbances (Dob) [67] disturbance frequency High Precision Cables and Servos • Not ﬁxed of • structure complexity Smaller extrapolation errors MLFFNN [59] • Instantaneous error Simpler structure • Multilayer Feed-forward • converges to zero slower Neural Network High reliability • than RBFN by approximately 1.5 s

Simplicity Lacks ﬁne motor control PD • • Versatility Trouble controlling higher (Proportional-Derivative) • • [68–70] Closed Loop order systems • Works well with a • PID [35,67] feedback system Nonlinear • Proportional-Integral- Most other controllers use Abrupt changes crash system • • Derivative this as a base

Multiple different types Electromagnetic & • Relays results to a computer (Arduino, raspberry pi, etc.) • Electroactive Microcontrollers [61] to record Different sizes available polymers (EAP) • Null position error under • PI [71] steady-state conditions Requires a low-pass filter • Proportional-Integral Closed loop Pressure Sensor • Cannot be by itself, must be Closed loop • Linear [72,73] • in a bigger unit

Estimation for elongation Accurate task • None (Theoretical Extended Kalman Filter • rate poor when model space coordinates Model) [74] is large

3. Materials and Construction With the increasing advancement of technology, much progress has been made in the design and construction of soft robotics. Previous generations of soft robotics utilized silicone rubber as their primary mode of construction [75–80]. Silicone is a thoroughly explored material for soft robotics [81] and is generally cast into molds for fabrication [82–84]. However, in recent years, new fabrics, materials, and methods have been beneficial in the construction of soft robotics [22,30,85–88], including 3D printed actuators [23–26,89–91], and combining multiple small soft actuators can be stronger than a single large actuator [92]. Newer materials such as those used for single mass granular materials for grippers [93,94] and liquid-phase polydimethylsiloxane also called PDMS [95] for microscale usage have been in development. More environmentally conscience materials have also been developed, that are also more benign to work with than traditional silicone [96]. Another new type of material is a composite of reduced Graphene Oxide (rGO) which provides prospective outlooks for the construction of soft self-healing devices as shown in Figure7[ 97]. Safer polymers have also been Actuators 2020, 9, 3 8 of 27 primary mode of construction [75–80]. Silicone is a thoroughly explored material for soft robotics [81] and is generally cast into molds for fabrication [82–84]. However, in recent years, new fabrics, materials, and methods have been beneficial in the construction of soft robotics [22,30,85–88], including 3D printed actuators [23–26,89–91], and combining multiple small soft actuators can be stronger than a single large actuator [92]. Newer materials such as those used for single mass granular materials for grippers [93,94] and liquid-phase polydimethylsiloxane also called PDMS [95] for microscale usage have been in development. More environmentally conscience materials have also been developed, that are also more benign to work with than traditional silicone [96]. Another new typeActuators of 2020material, 9, 3 is a composite of reduced Graphene Oxide (rGO) which provides prospective8 of 26 outlooks for the construction of soft self-healing devices as shown in Figure 7 [97]. Safer polymers have also been developed that are environmentally friendly and biodegradable [98]. These materials branchdeveloped away that from are the environmentally traditional silicone friendly rubber and material biodegradable and offer [98 ].new These incentives materials of branchconstruction away fromfrom the the ability traditional to self-repair, silicone rubber which material would andcut costs offer newin case incentives of damage of construction to the structure, from to the more ability cost to efficientself-repair, materials which wouldso that cut they costs could in case be ofmass damage produced to the structure,and readily to moreavailable cost eforffi cientmore materials people to so expandthat they their could field be massof research. produced Some and materials readily available can be variably for more stiffened, people to allowing expand their more field freedom of research. and specificitySome materials of design can be[99]. variably stiffened, allowing more freedom and specificity of design [99].

Figure 7. Present and future applications of self-healing materials for different wearable devices [97] Figure 7. Present and future applications of self-healing materials for different wearable devices [97] (Copyright 2017, John Wiley & Sons). (Copyright 2017, John Wiley & Sons). One of the main focus areas in the material construction of soft robotics is regarding the movement One of the main focus areas in the material construction of soft robotics is regarding the of the mechanism. The material must emulate the body groups such as a muscle or tendon of the movement of the mechanism. The material must emulate the body groups such as a muscle or tendon real-life counterpart [45,89,100–102] or the antagonistic relationship of both [103]. Many developments of the real-life counterpart [45,89,100–102] or the antagonistic relationship of both [103]. Many in the construction aspect has been on this criterion of the robotic mechanism [15,102]. This is especially developments in the construction aspect has been on this criterion of the robotic mechanism [15,102]. in the medical field where improvements are always needed [104]. Fortunately, there have been This is especially in the medical field where improvements are always needed [104]. Fortunately, advancements in the use of “hydrogels” to mimic the way that a real-life counterpart would move in there have been advancements in the use of “hydrogels” to mimic the way that a real-life counterpart nature [105,106]. These hydrogels have a better impact with more fluid and seamless movement as would move in nature [105,106]. These hydrogels have a better impact with more fluid and seamless opposed to the traditional use of silicone rubber that can sometimes offer a more robotic, and unnatural movement as opposed to the traditional use of silicone rubber that can sometimes offer a more sense of motion for the mechanism. The benefits of the hydrogel are that since it is a semi-solid material it can have more degrees of freedom of rotation as opposed to a rigid dynamic motion for the soft robotic mechanism. Other advanced materials being used in the field of soft robotics include electro-active polymers (EAP) which also provide excellent degrees of freedom and great compliance with its environments [31], and Diels-Alder Polymers which are able to “heal” microscopic and macroscopic damage [107]. Also, included in new material developments is an open-celled elastomeric foam which allows for great absorption by the material [108]. The main impact of what the improved technology with a more modern material design to soft robotics is in the benefit of undersea research explorations [109]. The current use of technology utilized Actuators 2020, 9, 3 9 of 26 by soft robotics is detrimental to the study and research of Deep Coral Reefs [109]. It cites the main reason being that the current material used for the soft robotics is often stiff and not as responsive under sea with high pressures as opposed to on land under normal atmospheric pressures commonplace to everyone. Resulting in the soft robotic destroying the coral reef sample, as it tries to interact with it. The solution proposed by the research staff discuss the use of sheet material such as “jet-cut aluminum” and “stacked layers of acrylic” these combinations of alloys results in the overall utility of the deep-sea robot to be able to gather and analyze better data without harming the coral habitat. While there are many other materials on the market that can achieve the desired effect for the soft robot to achieve operation [94] in its intended field. Most materials offer a wide range of response times to control the robotic specimen, the tensile strength of the material, and the overall durability while on the field of use. Many factors often play a crucial role in the overall success and development of the soft robotic structure [18]. Outlined in Table2 are the primary factors of the materials used in market soft robotic machines, how their composition is determined, and how the material is compared with other types.

Table 2. Summary of Diﬀerent Components of Testing for Soft Robotic Materials.

Material Purpose/Functionality Characteristic Pneumatic Flexible Finger Tube; Robot Chamber; Bidirectional motion Silicon Rubber [75–84] leg and gripper; Bi-bellows actuator; Locomotive and manipulative role; Buckling Linear Actuators Ecoflex-50; Elastosil Sheet Material [85] Pouch Motor Mass-fabricable; heat bonding; 3D printing materials Various 3D printing method type Soft pneumatic actuator; (NinjaFlex, EP, Nylon 12, and (FDM; DMP-SL; SLS; DLP); Flexible fluidic actuator EAA & AUD) [23–26,89–91] High degree of freedom Polychloroprene-based Hold the object without Single mass granular material gripper membrane [93,94] sensory feedback Microscale inward spiraling tentacle ~185 µm radius, PDMS [95] actuators ~0.78 mN grabbing force Environment friendly and 134 to 193 kPa UTS; PGSI [98] biodegradable polymers 57 to 131 kPa moduli; Smart material (Nitinol, PCL, Smart composite finger Discrete levels of stiffness Field’s metal) [99] DN Hydrogels; 3-DOF; easily customizable; delicate in Bending actuator Agrar/PAM [105,106] small millimeter scale; biocompatible Electro Active Polymer [31] OCTARM (artificial manipulator) PVDF based and dielectric Thermo-reversible covalent network; Diels-Alder Polymer [107] Self-healing elastomers heal micro and macroscopic damage Open-celled elastomeric Fluidic elastomer actuators Low density; foam [108]

The primary material in the construction of soft robotics has historically been silicone [110]. Figure8 is example of bending actuator that is made of silicone rubber. Many silicone-based materials have seen wide usage in industry [94] since the material is easily produced and very versatile. Although silicone is a versatile material, the longevity of the material is compromised when using silicone in a soft robot [105]. Another great soft robotic material is the 3D printed material as shown in Figure9. 3D printed soft robots can be easily mass produced, and due to the cost-eﬀective material more soft robots can be produced [22]. The drawback for a 3D printed material for a soft robotic part is that the tensile strength of the part is very low, and thus can break very easily as opposed to other materials for soft robotic parts [22]. However, with the advent of better 3D printing machines, new advances have been made to increase the tensile strength of 3D printed materials for soft robotics [13]. Actuators 2020, 9, 3 10 of 27

Open-celled elastomeric foam Fluidic elastomer actuators Low density; [108]

Actuators 2020The, 9, 3primary material in the construction of soft robotics has historically been silicone [110].10 of 27 Figure 8 is example of bending actuator that is made of silicone rubber. Many silicone-based materials Open-celledhave seen elastomeric wide usage foam in industry [94] since the material is easily produced and very versatile. Fluidic elastomer actuators Low density; Although[108] silicone is a versatile material, the longevity of the material is compromised when using silicone in a soft robot [105]. The primary material in the construction of soft robotics has historically been silicone [110]. Figure 8 is example of bending actuator that is made of silicone rubber. Many silicone-based materials have seen wide usage in industry [94] since the material is easily produced and very versatile.

AlthoughActuators silicone2020, 9, 3 is a versatile material, the longevity of the material is compromised when10 of 26using silicone in a soft robot [105].

Another great soft robotic material is the 3D printed material as shown in Figure 9. 3D printed soft robots can be easily mass produced, and due to the cost-effective material more soft robots can be produced [22]. The drawback for a 3D printed material for a soft robotic part is that the tensile strength of the part is very low, and thus can break very easily as opposed to other materials for soft FigureroboticFigure 8. parts Bi-bellows 8. [22].Bi-bellows However, bending bending withactuator actuatorthe advent manufactured manufactured of better using 3D using printing silicone silicone machines, rubber rubber (a new) ( ais) unpressurized, isadvances unpressurized, have been(b) is pressurizedmade(b) to is increase pressurized [79]. (Copyr the [ 79tensile].ight (Copyright 2011,strength Elsevier.) 2011, of 3D Elsevier.) printed materials for soft robotics [13].

FigureFigure 9. 9.Soft Soft roboticrobotic constructedconstructed byby 3D printing [[111]111] (Open Access).

4. Modeling

One of the biggest issues in the field of soft robotics is finding methods and models which are both accurate to experimental values and computationally viable [48,112–117]. Due to the deformable nature of under-actuated soft robotics, they are dynamically formulated using a system of infinite dimensions [23,58,93,118–120]. These designs are currently unable to be reduced to an exact model which often leads to rigid-body assumptions to create manageable dynamic equations [121]. Figure 10 below shows a McKibben muscle controlling a rigid body mechanism, allowing for the deformation of the actuator to be easily measured with rigid body dynamic equations [122]. Recently, setsFigure of 9. ordinary Soft robotic diff constructederential equations by 3D printing have been [111] formulated(Open Access). to create a more mathematically accurate model to describe the dynamic motion of soft robotics [119]. Currently, Finite Element Analysis (FEA) meshes /models have been the most widely used method of simulating soft robotic dynamics, an example seen below in Figure 11.

Actuators 2020, 9, 3 11 of 27

4. Modeling One of the biggest issues in the field of soft robotics is finding methods and models which are both accurate to experimental values and computationally viable [48,112–117]. Due to the deformable nature of under-actuated soft robotics, they are dynamically formulated using a system of infinite Actuatorsdimensions 2020, 9[23,58,93,118–120]., 3 These designs are currently unable to be reduced to an exact 11model of 27 which often leads to rigid-body assumptions to create manageable dynamic equations [121]. Figure 4. Modeling 10 below shows a McKibben muscle controlling a rigid body mechanism, allowing for the deformationOne of the of thebiggest actuator issues to inbe theeasily field measured of soft robotics with rigid is finding body dynamic methods equations and models [122]. which are both accurate to experimental values and computationally viable [48,112–117]. Due to the deformable nature of under-actuated soft robotics, they are dynamically formulated using a system of infinite dimensions [23,58,93,118–120]. These designs are currently unable to be reduced to an exact model which often leads to rigid-body assumptions to create manageable dynamic equations [121]. Figure 10Actuators below2020 shows, 9, 3 a McKibben muscle controlling a rigid body mechanism, allowing for11 ofthe 26 deformation of the actuator to be easily measured with rigid body dynamic equations [122].

Recently, sets of ordinary differential equations have been formulated to create a more mathematically accurate model to describe the dynamic motion of soft robotics [119]. Currently, Finite Element Analysis (FEA) meshes/models have been the most widely used method of simulating softFigure roboticFigure 10. Example10. dynamics, Example of a of rigid-bodyan a examplerigid-body robot seen robot arm below arm using using in McKibben Figure McKibben 11. artificial artificial muscle muscle [122]. [122]. (Copyright (Copyright 2018, 2018, Elsevier.) Elsevier.)

Figure 11.11. FEA model of a softsoft roboticrobotic actuatoractuator comparedcompared toto experimentexperiment [[123].123]. (a) SchematicSchematic of thethe chamber shape, (b) TheThe maximummaximum bending angles of thethe three actuators when deﬂated,deflated, (c) The FEA results ofof thethe threethree actuatorsactuators atat pressurizationpressurization statestate (20(20 kPa).kPa). (Open(Open Access).Access).

However, using FEM/FEA there is an exponentially increasing computational burden for However, using FEM/FEA there is an exponentially increasing computational burden for increasingly accurate models [90,124]. Which is why other areas of modeling have been explored, increasingly accurate models [90,124]. Which is why other areas of modeli ng have been explored, including the use of Artificial Neural Networks (ANNs) and Piecewise Constant Curvature (PCC) [32]. includingFigure the 11. useFEA ofmodel Artificial of a soft Neural robotic Networks actuator compared (ANNs) andto experiment Piecewise [123]. Constant (a) Schematic Curvature of the (PCC) Another aspect to consider when developing these models is the non-conservative forces at play [32]. chamberAnother shape, aspect (b to) The consider maximum when bending developing angles these of the models three actuators is the non-conservative when deflated, (c) forcesThe FEA at play including frictional forces between materials as well as the resistive forces of the soft materials, usually includingresults frictionalof the three forces actuators between at pressurization materials stateas we (20ll kPa). as the (Open resistive Access). forces of the soft materials, silicone or rubber [125]. To ignore these forces in the computational model directly leads to systematic usually silicone or rubber [125]. To ignore these forces in the computational model directly leads to error between the model and the experimental results. To mitigate these differences, more accurate However, using FEM/FEA there is an exponentially increasing computational burden for increasinglymodels which accurate can account models for [90,124]. the numerous Which forces is why that other are oftenareas overlooked of modeling for have simplicity been explored, purposes includingare being developedthe use of Artificial [125]. The Neural impedance Networks of the (ANNs) robot is consideredand Piecewise for simplicityConstant Curvature linear. However, (PCC) [32].most Another of the results aspect proposed to consider are wh easilyen developing generalizable these to themodels nonlinear is the non-conservative case [119]. forces at play includingIn Table frictional3, comparisons forces between can be seen materials between as diweffllerent as the gripping resistive technologies. forces of the This soft table materials, seeks to usuallyshow the silicone range and or rubber versatility [125]. diff Toerent ignore technologies these forces possess. in the In computational Figure12, for example, model directly ECF’s have leads very to small applied forces from them for very precise movements at the cost of high power consumption due to the high pressurization requirement that inherently comes with ECF based actuators [77,123,126,127]. Knowing which situations certain soft robotic technologies are appropriate is beneficial to the ability to accurately model and create designs. As more unique and effective methods are developed at producing soft robotic actuators, the ability to accurately predict the forces they produce in more elaborate designs becomes easier to achieve [3,27]. Actuators 2020, 9, 3 12 of 26

Actuators 2020, 9, 3 Table 3. Summary comparing diﬀerent soft robotic technologies. 12 of 27

systematic error betweenModeling the model andGripper the experiment size Objectal results. SuppliedTo mitigate theseForce differences,Surface Conditionsmore Technology accurate models which canMethod account for the(mm) numerousMass forces (g) that arePower often overlooked(N) for simplicity(Dry, Wet) purposes are being developedBeam Theory [125]. The impedance of the robot is considered for simplicity linear. Tendon-Based However, most of the results(Mathematical proposed are47 easi23ly generalizable No Object to th Ne/ nonlinearA 3.3 case [119]. Dry Stiﬀening [2] × In Table 3, comparisonsModel) can be seen between different gripping technologies. This table seeks to showTwisted-and-Coiled the range and versatility different technologies possess. In Figure 12, for example, ECF’s have Physics-Based N/A 1, 2 and 3 N/A 0.013 Dry veryActuator small [applied3] forces from them for very precise movements at the cost of high power Pneumaticconsumption Finger due [27 ]to the ANSYShigh pressurization 67 3.2 requirement 15.3 that inherently 12V comes 0.15 with ECF based Dry × actuators [77,123,126,127].Physics-Based Knowing which situations certain soft robotic technologies are appropriate SMA Coils [128] 80 55 N/A 0-5V N/A Both is beneficial to the abilityKinematics to accurately model× and create designs. As more unique and effective ECFmethods (electro-conjugate are developed at producing soft robotic actuators, the ability to accurately predict the forces Physics-Based 18 5 No Object .5kV-4kV 0.007 Dry they ﬂuid)produce [77] in more elaborate designs becomes× easier to achieve [3,27].

FigureFigure 12. 12. ExampleExample of of ECF ECF actuator actuator at atdifferent diﬀerent pressures pressures [77]. [(Open77]. (Open Access). Access).

Figures 13–15 showTable the 3. Summary accuracy comparing of computationally different soft robotic efficient technologies. models to experimental results of soft robotic actuators. In this case to make the FEA model computationally viable, the silicone Surface rubber was assumed toModeling be incompressible Gripper and size the helicalObject threadSupplied surrounding Force the silicone to be a radial Technology Conditions Method (mm) Mass (g) Power (N) constraint rather than a helical constraint [12]. Even with those approximations made(Dry, Wet) to the FEA model, Figure 13 shows the ability of FEA modeling techniques to represent the reverse pneumatic Beam Theory artificialTendon-BasedActuators muscles 2020 , (rPAM) 9, 3 effectively. This viability ofNo FEA models to predict the nature of13 of the 27 design (Mathematical 47 × 23 N/A 3.3 Dry will allowStiffening for [2] more complex, intricate and economicallyObject viable designs to be accurately modeled in the will allow for moreModel) complex, intricate and economically viable designs to be accurately modeled in future [12the]. future [12]. Twisted-and- Coiled Actuator Physics-Based N/A 1, 2 and 3 N/A 0.013 Dry [3]

Pneumatic ANSYS 67 × 3.2 15.3 12V 0.15 Dry Finger [27]

Physics-Based SMA Coils [128] 80 × 55 N/A 0-5V N/A Both Kinematics

ECF (electro- No conjugate fluid) Physics-Based 18 × 5 .5kV-4kV 0.007 Dry Object [77]

Figures 13–15 show the accuracy of computationally efficient models to experimental results of soft robotic actuators. In this case to make the FEA model computationally viable, the silicone rubber was assumed to be incompressible and the helical thread surrounding the silicone to be a radial constraint rather than a helical constraint [12]. Even with those approximations made to the FEA model, Figure 13 shows the ability of FEA modeling techniques to represent the reverse pneumatic artificial muscles (rPAM) effectively. This viability of FEA models to predict the nature of the design

Figure 13.FigureComparisons 13. Comparisons of di ofﬀ differenterent models models to to experimentalexperimental design design of reverse of reverse pneumatic pneumatic artificial artiﬁcial musclesmuscles (rPAMs) (rPAMs) [12] (Open [12] (Open Access). Access).

Figure 14. CAD model (Left) compared to prototype of a reverse pneumatic artificial muscle (rPAM) [12] (Open Access).

Actuators 2020, 9, 3 13 of 27

will allow for more complex, intricate and economically viable designs to be accurately modeled in the future [12].

ActuatorsFigure2020, 9 ,13. 3 Comparisons of different models to experimental design of reverse pneumatic artificial 13 of 26 muscles (rPAMs) [12] (Open Access).

FigureFigure 14. 14.CAD CAD model model (Left) (Left) compared compared to to prototype prototype of of a reversea reverse pneumatic pneumatic artiﬁcial artificial muscle muscle (rPAM) (rPAM) [12 ] Actuators(Open[12] 2020 (Open Access)., 9, 3 Access). 14 of 27

FigureFigure 15. 15. ExperimentalExperimental set-up set-up of of a a reverse reverse pneumatic pneumatic artificial artiﬁcial muscle (rPAM) [[12]12] (Open Access).

5. Sensors Sensors Over the past four years sensing technology for soft robotics, the lack of which has delayed industrial and commercial adaptation, has been heavilyheavily researched [[129].129]. There have been multiple research efforts efforts exploring different different curvature, tactile, and optical sensors for use with soft robotics yielding reliable sensors capable of a high degree of accuracy with with minimal minimal hysteresis hysteresis [55,118,130]. [55,118,130]. Many of the studies emphasized the development of inexpensive, easily manufacturable sensors that can be integrated into flexible flexible applications without affecting affecting the performance of the stretching and deforming structurestructure of of the the soft soft robots robots [33 [33,85,131–13,85,131–133].3]. The The primary primary avenues avenues of recent of recent research research have have been beeninvestigating investigating liquid liquid metal embeddedmetal embedded elastic sensors,elastic embeddedsensors, embedded magnetic sensors,magnetic semi-conductive sensors, semi- conductivepolymer sensors, polymer optical sensors, fiber optical curvature fiber sensors, curvature and sensors, even piezoelectric and even piezoelectric sensors [134 sensors,135]. [134,135]. Channels filledfilled withwith liquidliquid metal metal encased encased in in an an elastomer elastomer exhibit exhibit changes changes in resistancein resistance as theyas they are aredeformed deformed or stretched or stretched [136 ].[136]. This This behavior behavior is similar is similar to traditional to traditional strain strain gauges, gauges, though though liquid liquid metal metalembedded embedded elastomers elastomers (LMEE) (LMEE) exhibit exhibit superior superior flexibility flexibility and are and designed are designed to operate to operate in the in same the Young’ssame Young’s Modulus Modulus range range as soft as robots.soft robots. In Figure In Figu 16re, a16, sensor a sensor made made by by Edward Edward White White et al.et al. [136 [136]] is isdepicted. depicted. Note Note how how the the sensor sensor can can flex flex into into a tighta tight curve curve without without causing causing damage damage to to itself. itself.

Figure 16. Different views of a liquid metal embedded elastomer curvature sensor. (A) An overview of the sensor, (B) a close-up of the liquid metal filled channels in the sensor, (C) the sensor is able to flex and stretch without breaking [136] (Open Access).

Traditionally the silicone used for LMEEs would be cast into a mold to form the channels; however, this fabrication method takes a long time to generate new sensor patterns and can result in castings of uneven thickness. Instead, White et al. [136] spin cast their silicone, and then used a laser engraver to burn in the channels for the liquid metal. This method allows for the rapid iteration of

Actuators 2020, 9, 3 14 of 27

Figure 15. Experimental set-up of a reverse pneumatic artificial muscle (rPAM) [12] (Open Access).

5. Sensors Over the past four years sensing technology for soft robotics, the lack of which has delayed industrial and commercial adaptation, has been heavily researched [129]. There have been multiple research efforts exploring different curvature, tactile, and optical sensors for use with soft robotics yielding reliable sensors capable of a high degree of accuracy with minimal hysteresis [55,118,130]. Many of the studies emphasized the development of inexpensive, easily manufacturable sensors that can be integrated into flexible applications without affecting the performance of the stretching and deforming structure of the soft robots [33,85,131–133]. The primary avenues of recent research have been investigating liquid metal embedded elastic sensors, embedded magnetic sensors, semi- conductive polymer sensors, optical fiber curvature sensors, and even piezoelectric sensors [134,135]. Channels filled with liquid metal encased in an elastomer exhibit changes in resistance as they are deformed or stretched [136]. This behavior is similar to traditional strain gauges, though liquid metal embedded elastomers (LMEE) exhibit superior flexibility and are designed to operate in the

sameActuators Young’s2020, Modulus9, 3 range as soft robots. In Figure 16, a sensor made by Edward White14 et of al. 26 [136] is depicted. Note how the sensor can flex into a tight curve without causing damage to itself.

Figure 16. Different views of a liquid metal embedded elastomer curvature sensor. (A) An overview of Figure 16. Different views of a liquid metal embedded elastomer curvature sensor. (A) An overview the sensor, (B) a close-up of the liquid metal filled channels in the sensor, (C) the sensor is able to flex of the sensor, (B) a close-up of the liquid metal filled channels in the sensor, (C) the sensor is able to and stretch without breaking [136] (Open Access). flex and stretch without breaking [136] (Open Access). Actuators 2020, 9,Traditionally 3 the silicone used for LMEEs would be cast into a mold to form the channels; however, 15 of 27 thisTraditionally fabrication method the silicone takes a used long timefor toLMEEs generate would new sensorbe cast patterns into a and mold can resultto form in castings the channels; of sensorhowever, designuneven and this thickness. fabricationprecision Instead, manufacture method White ta etkes al. [aof136 long the] spin timese castnsor to their generatecomponents. silicone, new and sensor thenA layered used patterns a laser design and engraver can was result toused, in as burn in the channels for the liquid metal. This method allows for the rapid iteration of sensor design in Figurecastings 17. of uneven thickness. Instead, White et al. [136] spin cast their silicone, and then used a laser engraverand precision to burn manufacture in the channels of the for sensor the components.liquid metal. A This layered method design allows was used, for asthe in rapid Figure iteration17. of

Figure 17. The construction stack of liquid metal embedded elastomer curvature sensor [136] (Open Access). Figure 17. The construction stack of liquid metal embedded elastomer curvature sensor [136] (Open Access). Embedded magnets coupled with Hall Effect sensors have proved to be an accurate method for providing tactile and curvature sensing. Small rare earth magnets and Hall Effect sensors are Embeddedinexpensive magnets and accessible, coupled whilewith alsoHall providing Effect sensors rapid feedbackhave proved and precise to be measurementan accurate [method137]. for providingWhen tactile integrated and curvature into a soft roboticsensing. hand, Small tactile ra 3re axis earth sensors magnets provide accurateand Hall and Effect detailed sensors data are regarding the contact forces, allowing the manipulation of delicate objects [138]. These types of sensors inexpensiveare generallyand accessible, manufactured while using also silicone providing rubber castrapid into feedback 3D printed and molds precise [137–139 measurement]. The magnets [137]. When integratedare suspended into ina thesoft silicone, robotic as hand, in Figure tactile 18. 3 axis sensors provide accurate and detailed data regarding theEmbedded contact forces, magnet sensorsallowing (EMS) the measuremanipula thetion displacement of delicate of the objects magnetic [138]. field These to detect types of sensors aremovement generally and manufactured are therefore sensitive using to silicone external forces rubber as well cast as into internal 3D forces.printed EMS molds have proved[137–139]. to The magnets arebe highly suspended accurate in with the minimal silicone, intrusion as in onFigure the flexibility 18. of the soft robot itself, and also to be highly model-able, allowing for good predictions of functionality, as in Figure 19.

Figure 18. A tactile sensor using a magnet and three axis Hall Effect sensor. In this case, the soft sensor is incorporated into an existing hard robot to interface with delicate objects [139]. Hybrid Robots such as the above and [78,138] offer exciting opportunities for innovation (Open Access).

Embedded magnet sensors (EMS) measure the displacement of the magnetic field to detect movement and are therefore sensitive to external forces as well as internal forces. EMS have proved to be highly accurate with minimal intrusion on the flexibility of the soft robot itself, and also to be highly model-able, allowing for good predictions of functionality, as in Figure 19. B C

Figure 19. The function of a hall effect sensor in an EMS (A), the distance between the magnet and hall effect sensor in the curvature sensor presented by Ozel et al. (B), and the data correlating the accuracy of the models to the curvature sensor design (C) [137] (Open Access).

Actuators 2020, 9, 3 15 of 27 sensor design and precision manufacture of the sensor components. A layered design was used, as in Figure 17.

Figure 17. The construction stack of liquid metal embedded elastomer curvature sensor [136] (Open Access).

Embedded magnets coupled with Hall Effect sensors have proved to be an accurate method for providing tactile and curvature sensing. Small rare earth magnets and Hall Effect sensors are inexpensive and accessible, while also providing rapid feedback and precise measurement [137]. WhenFigure integrated 17. The into construction a soft robotic stack of hand, liquid tactile metal embe3 axisdded sensors elastomer provide curvature accurate sensor and [136] detailed (Open data regardingAccess). the contact forces, allowing the manipulation of delicate objects [138]. These types of sensors are generally manufactured using silicone rubber cast into 3D printed molds [137–139]. The magnetsEmbedded are suspended magnets in coupled the silicone, with asHall in FigureEffect sensors 18. have proved to be an accurate method for providing tactile and curvature sensing. Small rare earth magnets and Hall Effect sensors are inexpensive and accessible, while also providing rapid feedback and precise measurement [137]. When integrated into a soft robotic hand, tactile 3 axis sensors provide accurate and detailed data regarding the contact forces, allowing the manipulation of delicate objects [138]. These types of Actuatorssensors2020 are, generally9, 3 manufactured using silicone rubber cast into 3D printed molds [137–139].15 ofThe 26 magnets are suspended in the silicone, as in Figure 18.

Embedded magnet sensors (EMS) measure the displacement of the magnetic field to detect movement and are therefore sensitive to external forces as well as internal forces. EMS have proved Figure 18. A tactile sensor using a magnet and three axis Hall Eﬀect sensor. In this case, the soft sensor to be Figurehighly 18. accurate A tactile with sensor minimal using a magnetintrusion and on three the axis flexibility Hall Effect of sensor.the soft In robot this case, itself, the andsoft sensoralso to be is incorporated into an existing hard robot to interface with delicate objects [139]. Hybrid Robots such is incorporated into an existing hard robot to interface with delicate objects [139]. Hybrid Robots such highlyas model-able, the above and allowing [78,138] o forﬀer good exciting predictions opportunities of functionality, for innovation (Openas in Figure Access). 19. as the above and [78,138] offer exciting opportunities for innovation (Open Access).

Embedded magnet sensorsB (EMS) measure the displacement of theC magnetic field to detect movement and are therefore sensitive to external forces as well as internal forces. EMS have proved to be highly accurate with minimal intrusion on the flexibility of the soft robot itself, and also to be highly model-able, allowing for good predictions of functionality, as in Figure 19. B C

Figure 19. The function ofof aa hallhall e ﬀeffeectct sensor sensor in in an an EMS EMS (A (),A the), the distance distance between between the the magnet magnet and and hall halleﬀect effect sensor sensor in the in curvature the curvature sensor sensor presented presented by Ozel by et Ozel al. (B et), andal. (B the), and data the correlating data correlating the accuracy the Actuatorsaccuracyof the 2020 models, 9of, 3 the to models the curvature to the curvature sensor design sensor (C design)[137] (Open(C) [137] Access). (Open Access). 16 of 27

SemiSemi conductive polymers polymers have have been been used used to cons to constructtruct both both tactile tactile and curvature and curvature sensors. sensors. They Theyoperate operate under under the function the function that compressing that compressing the ma thetter matter of a ofsemi-conductive a semi-conductive matrix matrix will will drive drive the theconductive conductiveFigure 19.particles The particles function either either of acloser hall closer effe together,ct together, sensor indecre decreasingan EMSasing (A resistance,), resistance,the distance or or between farther farther the apart, magnet increasingincreasing and resistance,resistance,hall effect asas inin sensor FigureFigure in 2020.the. Either Eithercurvature changechange sensor cancan presented bebe detecteddetected by Ozel andand utilizedetutilized al. (B), to toand locatelocate the data thethe contactcontactcorrelating oror estimate estimatethe thethe curvaturecurvatureaccuracy of [[78,110,140,141].78 the,110 models,140,141 to the]. TheseThes curvaturee sensorssensors sensor cancan design bebe 3D3D (C printed) [137] (Open with Access). multi material printing [[11,110],11,110], or or byby mixingmixing carboncarbon powderpowder withwith siliconesilicone [[140],140], or through the use of conductive hydrogels [[141].141]. TheseThese sensorssensors provideprovide good good consistency consistency with with a widea wide range range of sensitivityof sensitivity and and curvature curvature constraints constraints for diforﬀ erentdifferent applications, applications, as in as Figure in Figure 21. A 21. soft A robotic soft robotic gripper gripper is constructed is constructed and tested and usingtested a using similar a sensorsimilar technology sensor technology and fabrication and fabrication method method by Shih by et al.Shih [142 et]. al. [142].

FigureFigure 20.20. The eeffectﬀect of pressurepressure onon aa semi-conductivesemi-conductive matrixmatrix [[140]140] (Open(Open Access).Access).

Figure 21. 3D multi material prints can be leveraged to create embedded sensors within soft robotic actuators capable of holding many different objects (A–E), 3D perspective of the scatter plot (F) and 2D perspective of the scatter plot (G) [11]. Another similar project was completed in 2015 by Homberg et al. [143] and again in 2019 by Truby et al. [144] (Open Access).

The use of carbon black suspended in silicone to construct contact sensors presents its own challenges. The foremost of these is the consistent homogeneity of the carbon-silicon blend [140], which is extremely important to the reliability of the sensor. Devaraj et al. found that different carbon

Actuators 2020, 9, 3 16 of 27

Semi conductive polymers have been used to construct both tactile and curvature sensors. They operate under the function that compressing the matter of a semi-conductive matrix will drive the conductive particles either closer together, decreasing resistance, or farther apart, increasing resistance, as in Figure 20. Either change can be detected and utilized to locate the contact or estimate the curvature [78,110,140,141]. These sensors can be 3D printed with multi material printing [11,110], or by mixing carbon powder with silicone [140], or through the use of conductive hydrogels [141]. These sensors provide good consistency with a wide range of sensitivity and curvature constraints for different applications, as in Figure 21. A soft robotic gripper is constructed and tested using a similar sensor technology and fabrication method by Shih et al. [142].

Actuators 2020, 9, 3 16 of 26 Figure 20. The effect of pressure on a semi-conductive matrix [140] (Open Access).

Figure 21. 3D multi material prints can be leveraged to create embedded sensors within soft robotic Figure 21. 3D multi material prints can be leveraged to create embedded sensors within soft actuatorsrobotic capable actuators of holding capable many of holding different many objects different (A–E), 3D objects perspective (A–E of), the3D scatterperspective plot (F )of and the 2D perspective of the scatter plot (G)[11]. Another similar project was completed in 2015 by Homberg et Actuatorsscatter 2020 plot, 9, 3 (F) and 2D perspective of the scatter plot (G) [11]. Another similar project was17 of 27 al. [143] and again in 2019 by Truby et al. [144] (Open Access). completed in 2015 by Homberg et al. [143] and again in 2019 by Truby et al. [144] (Open Access). concentrations in the mixture yielded different sensitivities to pressure and noise, the relation of The use of carbon black suspended in silicone to construct contact sensors presents its own whichThe can use be ofobserved carbon inblack Figure suspended 22. Ultimately in silicone it was tofound construct that a 10%contact carbon sensors black presents mixture providesits own challenges.challenges.a noisier response, The The foremost foremost but faster of of these these and iswithis thethe lower consistentconsistent hysteresis homogeneity than a 5% ofmixture of the the carbon-silicon carbon-silicon [140]. blend blend [140], [140 ], whichwhich isConductive extremelyis extremely important hydrogels important to to(CH) the the reliability reliabilityfunction of similarlof thethe sensor.sensor.y to theDevaraj carbon et et al. al.black found found and that that siliconedifferent different mixture, carbon carbon concentrations in the mixture yielded different sensitivities to pressure and noise, the relation of which however, the capacitance of the material can be fine-tuned depending on the ions used to make the canCH; be observed additionally, in Figure CHs are22. biocompatible, Ultimately it wasallowing found further that a exploration 10% carbon of black soft mixturerobotics into provides the a noisiermedical response, field [141]. but CH faster structures and with are lower cast into hysteresis molds for than manufacture. a 5% mixture [140].

FigureFigure 22. 22.The The relationship relationship between between resistance resistance and and carbon carbon black black inin aa siliconesilicone contact sensor. sensor. Greater Greater concentrationsconcentrations of Carbonof Carbon Black, Black, a semi-conductivea semi-conductive silicone siliconerubber rubber additive,additive, resu resultlt in in lower lower resistivity, resistivity, andand therefore therefore more more sensitivity, sensitivity, but but also also more more sensor sensor noise noise [140 [140]] (Open (Open Access). Access).

Fiber optic sensors are promising avenues of sensor research because of their flexibility, negligible hysteresis, and speed. Optical sensors may either visually track the soft robot [28], or may rely on detecting wavelength shift in a fiber optic cable due to the stretching and bending of the fiber optic [145,146]. Visual tracking tactile sensors rely on point capture systems on the interior of the sensor wall, as in the Tactip sensors [28]. These sensors are 3D printed using a dual extrusion process to print flexible black material and hard white material to provide accurate and detailed point-maps for the camera to detect. Any deviation from the normal pattern of white dots indicates contact, as in Figure 23. This method of contact sensing can be extremely accurate; however, it does require image processing, which must be handled with supplementary computers.

Actuators 2020, 9, 3 17 of 26

Conductive hydrogels (CH) function similarly to the carbon black and silicone mixture, however, the capacitance of the material can be fine-tuned depending on the ions used to make the CH; additionally, CHs are biocompatible, allowing further exploration of soft robotics into the medical field [141]. CH structures are cast into molds for manufacture. Fiber optic sensors are promising avenues of sensor research because of their flexibility, negligible hysteresis, and speed. Optical sensors may either visually track the soft robot [28], or may rely on detecting wavelength shift in a fiber optic cable due to the stretching and bending of the fiber optic [145,146]. Visual tracking tactile sensors rely on point capture systems on the interior of the sensor wall, as in the Tactip sensors [28]. These sensors are 3D printed using a dual extrusion process to print flexible black material and hard white material to provide accurate and detailed point-maps for the camera to detect. Any deviation from the normal pattern of white dots indicates contact, as in Figure 23. Actuators 2020, 9, 3 18 of 27 ThisActuators method 2020, of 9, contact3 sensing can be extremely accurate; however, it does require image processing,18 of 27 which must be handled with supplementary computers.

Figure 23. TacTip endoscopic visual contact sensorsensor [[28]28] (Open(Open Access).Access). Figure 23. TacTip endoscopic visual contact sensor [28] (Open Access). Fiber optic cables are used to detect curvature in a soft robot structure, and are known as Fiber Bragg Fiber Gratings, optic or cables FBGs are [[145].145 used]. FBGs to detect operate curvature based inon a detecting soft robot shifts structure, in wavelength and are known as the as optic Fiber cableBragg isis bentbentGratings, [[146].146]. or FiberFiber FBGs opticoptic [145]. cablescables FBGs are are operate thin thin and and based ﬂexible, flexible, on anddetecting and therefore therefore shifts do do notin notwavelength reduce reduce the the freedomas freedom the optic of theofcable the soft soft is actuator bent actuator [146]. itself. itself. Fiber FBGs FBGs optic are are cables also also resistant resistantare thin to toand external external flexible, interference, interference, and therefore comparedcompared do not totoreduce electromagneticelectromagnetic the freedom sensorsof the soft [[145].145 actuator]. In Figure itself. 2424, ,FBGs the installation are also resistant of an FBGFB toG external sensor interference, into a soft roboticrobotic compared actuatoractuator to electromagnetic isis presented.presented. sensors [145]. In Figure 24, the installation of an FBG sensor into a soft robotic actuator is presented.

Figure 24. A pneumatic softsoft actuatoractuator employingemploying a a FBG FBG curvature curvature sensor sensor to to measure measure the the deformation deformation of theofFigure the soft soft actuator 24. actuator A pneumatic [145 [145]] (Open (Open soft Access). actuator Access). employing a FBG curvature sensor to measure the deformation of the soft actuator [145] (Open Access). As shownshown in in the the Table Table4, various 4, various sensors sensors are compared are compared based on based di ﬀerent on different mechanisms, mechanisms, materials, functionalities.materials,As shownfunctionalities. Four in ditheﬀerent TableFour types different4, ofvarious mechanisms types sensors of mechanisms are are reviewed compared are in this reviewed based paper: on Resistive,in differentthis paper: Piezoresistive, mechanisms, Resistive, Magnetic,Piezoresistive,materials, and functionalities. Optical.Magnetic, Even and Four if theOptical. different mechanism Even types if is of thethe mechanisms same,mechanism each are paper is reviewed the that same, is referencedin eachthis paper: paper in theResistive, that table is referencedPiezoresistive, in the tableMagnetic, uses differentand Optical. materials Even and if aimsthe formechanism different functionality.is the same, Theeach most paper common that is functionalitiesreferenced in arethe curvature,table uses different tactile, and materials strain sensors.and aims for different functionality. The most common functionalities are curvature, tactile, and strain sensors. Table 4. Summary of Different Soft Sensor Mechanisms and Materials. Table 4. Summary of Different Soft Sensor Mechanisms and Materials. Transducer Transducer Material Functionality Characteristic Performance mechanism Material Functionality Characteristic Performance mechanism Linearity and low couplingLinearity between and low Laser engraved coupling between Resistive Strain, Laser engraved summation and Resistive Liquid metal [136] Strain, microchannel, Flexible summation and sensors Liquid metal [136] Curvature microchannel, Flexible differential channels in sensors Curvature system differential channels in system response to strain and curvature.response to strain and curvature.

Actuators 2020, 9, 3 18 of 26 uses diﬀerent materials and aims for diﬀerent functionality. The most common functionalities are curvature, tactile, and strain sensors.

Table 4. Summary of Diﬀerent Soft Sensor Mechanisms and Materials.

Transducer Material Functionality Characteristic Performance Mechanism Linearity and low coupling Laser engraved between summation and Liquid metal [136] Strain, Curvature microchannel, Flexible differential channels in Resistive system response to strain sensors and curvature. Conductive Hydrogel has Location of touch points Conductive Touch location tensile elastic modulus of are determined by its polar Hydrogel [141] stretch 1.335 kPa; Gel resistance radius increases with stretching. Photopolymers 3D–printed, Multimaterial (Tango+, Tangoblack+, Strain – with various conductivities VeroClear, SUP705) [11] 3D–printed (FDM), high Detectable pressure Range: sensitivity, excellent Composite (TPU & 292 Pa to 487 kPa Piezoresistive Tactile recovery to bending strain, PLA–G) [110] Bending angle range: sensors wide range of pressure 0.1 –26.3 detecting ◦ ◦ Composite (Carbon Controllable composite Response rise time upon Elastomeric force black) [140] film thickness applied load: 600 ms Sensitivity with noise Hall sensors and filtering: 0.0012 cm 1 permanent Curvature Contract–free − Magnetic Sensitivity without noise magnets [137] 1 filtering: 0.05 cm− High sensitivity, low Hall sensors and Minimum Sensed force: hysteresis, good permanent Tactile 7.2 mN; Recovery time: 0.3 s; repeatability, magnets [138] Noise level: 2.5 mN Easy fabrication ± 3D printed, Photopolymer Sensor Localization accuracy Tactile Biomimetic Morphology, (TangoBlack+)[28] average: 0.205 Optical High accuracy ± Range of sensitivity of the FBG, Polyimide Reliable sensitivity, good sensor: 1.96 to 50.65 pm/m 1 Curvature − film [145] repeatability The curvature ranges 1 up to 30 m− Range of sensitivity of the FBG, Polyimide Curvature Flexible system sensor: 9.73 to 212.8 pm/m 1 film [146] − Sensor error average: 1.82%

6. Summary and Outlook The goal of this review work is to provide various information of soft pneumatic actuators to new researchers and innovators in the field of soft robotics, and to help experienced researchers attempting to stay up to date on recent advances in their field. This review has covered some of the recent developments in soft robotics to serve as a compendium of knowledge and a springboard for others in the field. The developments facilitated the widespread adoption of soft robotics in the industry, primarily maximizing repeatability, manufacturability, and of adequate control and stability of the robot. Soft robotics provided a solution to human-robot interfaces in a safe and effective manner and serve a multitude of functions in medical, industrial, or commercial applications [147–150]. Also, integration of various new technologies has helped push the boundaries of soft robotics. Many of soft robotics projects ultimately attempted to make soft robotics more controllable, more predictable, more robust, and more manufacturable through innovation and study [151–154]. In addition to various attempts, there is still much to learn and explore in the field of soft robotics, particularly in regards to flexible Actuators 2020, 9, 3 19 of 26 power supplies, flexible drive components [147], and onboard controllers, which represent the next obstacles to the commercialization of soft robotics [10,21].

Author Contributions: Conceptualization, M.S., J.W., and C.H.; methodology, J.W.; validation, All; formal analysis, M.S., J.W., and T.Z.; investigation, All; resources, All; data curation, All; writing—original draft preparation, J.W., T.Z., C.H., F.S.S., S.K., S.Y.; writing—review and editing, J.W., T.Z., M.S.; supervision, M.S.; project administration, M.S. All authors have read and agreed to the published version of the manuscript. Acknowledgments: The authors would like to thank the Mechanical Engineering Department at Georgia Southern University for their dedication to and support for undergraduate research. Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results, since no funding was received.

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