Compared with rigid robots, soft robots are inherently compliant and have advantages in the tasks requiring flexibility and safety. But sensing the high dimensional body deformation of soft robots is a challenge. Encasing soft strain sensors into the internal body of soft robots is the most popular solution to address this challenge. But most of them usually suffer from problems like nonlinearity, hysteresis, and fabrication complexity. To endow the soft robots with body movement awareness, this work presents a bioinspired architecture by taking cues from human proprioception system. Differing from the popular usage of smart material-based sensors embedded in soft actuators, we created a synthetic analog to the human muscle system, using paralleled soft pneumatic chambers to serve as receptors for sensing body deformation. We proposed to build the system with redundant receptors and explored deep learning tools for generating the kinematic model. Based on the proposed methodology, we demonstrated the design of three degrees of freedom continuum joint and how its kinematic model was learned from the unified pressure information of the actuators and receptors. In addition, we investigated the response of the soft system to receptor failures and presented both hardware and software level solutions for achieving graceful degradation. This approach offers an alternative to enable soft robots with proprioception capability, which will be useful for closed-loop control and interaction with environment.
Get full access to this article
View all access options for this article.
References
1.
TaylorJL. Proprioception. In: Encyclopedia of Neuroscience. (Binder MD, Hirokawa N, Windhorst U, et al. eds.) Springer: Berlin/Heidelberg; 2009; pp. 1143–1149.
2.
ProskeU, GandeviaSC. The proprioceptive senses: Their roles in signaling body shape, body position and movement, and muscle force. Physiol Rev, 2012; 92(4):1651–1697.
3.
XieZ, DomelAG, AnN, et al.Octopus arm-inspired tapered soft actuators with suckers for improved grasping. Soft Robot, 2020; 7(5):639–648.
BeccaiL, LucarottiC, TotaroM, et al.Soft robotics mechanosensing. In: Soft Robotics: Trends, Applications and Challenges: Proceedings of the Soft Robotics Week, April 25–30, 2016, Livorno, Italy (Biosystems and Biorobotics). (Laschi C, Rossiter J, Iida F, et al. eds.) Springer: New York, NY; 2017; pp. 47–56.
8.
BahramzadehY, ShahinpoorM. A review of ionic polymeric soft actuators and sensors. Soft Robot, 2014; 1(1):38–52.
9.
WangJ, GaoD, LeePS. Recent progress in artificial muscles for interactive soft robotics. Adv Mater, 2021; 33(19):2003088; doi: 10.1002/adma.202003088
10.
WakimotoS, MisumiJ, SuzumoriK. New concept and fundamental experiments of a smart pneumatic artificial muscle with a conductive fiber. Sens Actuators A Phys, 2016; 250:48–54.
11.
CianchettiM, RendaF, LicofonteA, et al.Sensorization of continuum soft robots for reconstructing their spatial configuration. In: 2012 IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Rome, Italy. IEEE; 2012; pp. 634–639.
12.
KureK, KandaT, SuzumoriK, et al.Flexible displacement sensor using injected conductive paste. Sens Actuators A Phys, 2008; 143(2):272–278.
13.
LarsonC, PeeleB, LiS, et al.Highly stretchable electroluminescent skin for optical signaling and tactile sensing. Science, 2016; 351(6277):1071–1074.
14.
AtalayA, SanchezV, AtalayO, et al.Batch fabrication of customizable silicone-textile composite capacitive strain sensors for human motion tracking. Adv Mater Technol, 2017; 2(9):1700136.
15.
AtalayO, AtalayA, GaffordJ, et al.A highly stretchable capacitive-based strain sensor based on metal deposition and laser rastering. Adv Mater Technol, 2017; 2(9):1700081.
16.
HelpsT, RossiterJ. Proprioceptive flexible fluidic actuators using conductive working Fluids. Soft Robot, 2018; 5(2):175–189.
17.
RussoS, RanzaniT, LiuH, et al.Soft and stretchable sensor using biocompatible electrodes and liquid for medical applications. Soft Robot, 2015; 2(4):146–154.
18.
KramerRK, MajidiC, SahaiR, et al.Soft curvature sensors for joint angle proprioception. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (ICRA), San Francisco, CA, USA. IEEE; 2011; pp. 1919–1926.
19.
TrubyRL, WehnerM, GrosskopfAK, et al.Soft somatosensitive actuators via embedded 3D printing. Adv Mater, 2018; 30(15):1870106.
20.
YangY, ChenY. Innovative design of embedded pressure and position sensors for soft actuators. IEEE Robot Autom Lett, 2017; 3(2):656–663.
21.
HainsworthT, SmithL, AlexanderS, et al.A fabrication free, 3D printed, multi-material, self-sensing soft actuator. IEEE Robot Autom Lett, 2020; 5(3):4118–4125.
22.
ScharffRBN, DoornbuschRM, DoubrovskiEL, et al.Color-based proprioception of soft actuators interacting with objects. IEEE ASME Trans Mechatron, 2019; 24(5):1964–1973.
23.
WangH, ZhangR, ChenW, et al.Shape detection algorithm for soft manipulator based on fiber Bragg gratings. IEEE ASME Trans Mechatron, 2016; 21(6):2977–2982.
24.
RyuSC, DupontPE. FBG-based shape sensing tubes for continuum robots. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China. IEEE; 2014; pp. 3531–3537.
25.
ChitaliaY, DeatonNJ, JeongS, et al.Towards FBG-based shape sensing for micro-scale and meso-scale continuum robots with large deflection. IEEE Robot Autom Lett, 2020; 5(2):1712–1719.
26.
ChenW, XiongC, LiuC, et al.Fabrication and dynamic modeling of bidirectional bending soft actuator integrated with optical waveguide curvature sensor. Soft Robot, 2019; 6(4):495–506.
27.
ZhaoH, O'BrienK, LiS, et al.Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides. Sci Robot, 2016; 1(1):eaai7529.
FeltW, RemyCD, TelleriaMJ, et al.An inductance-based sensing system for bellows-driven continuum joints in soft robots. Auton Robot, 2019; 43(2):435–448.
30.
CroomJM, RuckerDC, RomanoJM, et al.Visual sensing of continuum robot shape using self-organizing maps. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA. IEEE; 2010; pp. 4591–4596.
31.
CamarilloDB, LoewkeKE, CarlsonCR, et al.Vision based 3-D shape sensing of flexible manipulators. In: 2008 IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, USA. IEEE; 2008; pp. 2940–2947.
32.
WangR, WangS, DuS, et al.Real-time soft body 3D proprioception via deep vision-based sensing. IEEE Robot Autom Lett, 2020; 5(2):3382–3389.
33.
TawkC, PanhuisM, SpinksGM, et al.Soft pneumatic sensing chambers for generic and interactive human–machine interfaces. Adv Intell Syst, 2019; 1(1):1900002.
34.
TawkC, ZhouH, SariyildizE, et al.Design, modeling, and control of a 3D printed monolithic soft robotic finger with embedded pneumatic sensing chambers. IEEE ASME Trans Mechatron, 2020; 26(2):876–887.
35.
YangH, ChenY, SunY, et al.A novel pneumatic soft sensor for measuring contact force and curvature of a soft gripper. Sens Actuator A Phys, 2017; 266:318–327.
36.
TenzerY, JentoftLP, HoweRD. The feel of MEMS barometers: Inexpensive and easily customized tactile array sensors. IEEE Robot Autom Mag, 2014; 21(3):89–95.
37.
ChinK, HellebrekersT, MajidiC. Machine learning for soft robotic sensing and control. Adv Intell Syst, 2020; 2(6):1900171.
38.
KimDW, ParkM, ParkYL. Probabilistic modeling and Bayesian filtering for improved state estimation for soft robots. IEEE Trans Robot, 2021; 37(5):1728–1741.
39.
HanS, KimT, KimD, et al.Use of deep learning for characterization of microfluidic soft sensors. IEEE Robot Autom Lett, 2018; 3(2):873–880.
40.
FangG, TianY, YangZX, et al.Efficient Jacobian-based inverse kinematics with sim-to-real transfer of soft robots by learning. IEEE ASME Trans Mechatron, 2022; 27(6):5296–5306; doi: 10.1109/TMECH.2022.3178303
41.
Van MeerbeekIM, De SaCM, ShepherdRF. Soft optoelectronic sensory foams with proprioception. Sci Robot, 2018; 3(24):eaau2489.
42.
ScharffRBN, FangG, TianY, et al.Sensing and reconstruction of 3-D deformation on pneumatic soft robots. IEEE ASME Trans Mechatron, 2021; 26(4):1877–1885.
43.
SundaramS, KellnhoferP, LiY, et al.Learning the signatures of the human grasp using a scalable tactile glove. Nature, 2019; 569(7758):698–702.
44.
ThuruthelTG, ShihB, LaschiC, et al.Soft robot perception using embedded soft sensors and recurrent neural networks. Sci Robotics, 2019; 4(26):eaav1488.
45.
SoterG, ConnA, HauserH, et al.Bodily aware soft robots: Integration of proprioceptive and exteroceptive sensors. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia. IEEE; 2018; pp. 2448–2453.
46.
LooJY, DingZY, BaskaranVM, et al.Robust multimodal indirect sensing for soft robots via neural network-aided filter-based estimation. Soft Robot, 2022; 9(3):591–612.
47.
SakuraiR, NishidaM, SakuraiH, et al.Emulating a sensor using soft material dynamics: A reservoir computing approach to pneumatic artificial muscle. In: 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft), New Haven, CT, USA. IEEE; 2020; pp. 710–717.
48.
SoterG, HauserH, ConnA, et al.Shape reconstruction of CCD camera-based soft tactile sensors. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA. IEEE; 2020; pp. 8957–8962.
49.
GrünewaldRA, YonedaY, ShipmanJM, et al.Idiopathic focal dystonia: A disorder of muscle spindle afferent processing. Brain, 1997; 120(12):2179–2185.
50.
LephartS, FuF. Proprioception and Neuromuscular Control in Joint Stability. Champaign, IL: Human Kinetics;. 2000.
51.
WangL, WangZ. Mechanoreception for soft robots via intuitive body cues. Soft Robot, 2020; 7:198–217.
52.
MarcheseAD, KomorowskiK, OnalCD, et al.Design and control of a soft and continuously deformable 2D robotic manipulation system. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China. IEEE; 2014; pp. 2189–2196.
53.
ChenX, GuoY, DuanmuD, et al.Design and modeling of an extensible soft robotic arm. IEEE Robot Autom Lett, 2019; 4(4):4208–4215.
54.
HochreiterS, SchmidhuberJ. Long short-term memory. Neural Comput, 1997; 9(8):1735–1780.
55.
ChungJ, GulcehreC, ChoKH, et al.Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv: 1412.3555, 2014.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
