Deformation-based Multimodal Perception for Soft Pneumatic Robots

苏引引

SU Yinyin

12050014

博士

Robotics and control

戴建生

机械与能源工程系

Prof. James Lam

The University of Hong Kong

2024-08-27

2024-09-12

The University of Hong Kong

Hong Kong

Soft robots made of highly compliant and elastic materials have attracted attention in the robotics field. Compared to the rigid-body robotic systems composed of rigid links, soft robots exhibit more advantages in terms of complicated tasks requiring safety and interacting with humans and unstructured environments. Because soft robots have adaptability and flexibility resulting from inherited compliance. However, soft robots still require sensory feedback. It can enable soft robots to accomplish complicated tasks, like accurate closed-loop control, by understanding their surroundings and monitoring their real-time self-states.

Although various previous research works have recently been reported to endow soft robots with single-type robotic perception by accompanying the internal and external sensors, the high-level multimodal perception for the soft robots by embedded sensors is still challenging. (1) Existing self-sensing soft robots integrate sensors into soft actuators where the heterogeneous stiffness between the sensors and actuators leads to complicated fabrication and even attenuates the soft robot’s compliance. (2)The modeling of soft robots’ state estimation to achieve multimodal sensing is still difficult due to the soft material’s inherited nonlinearity and strong decoupling between sensing modalities. (3) Construction of the robust perception strategy for soft robots is required to enable them to complete tasks still when partial subsystem failure occurs. (4) Once the sensing abilities are developed for soft robots, the high-level closed-loop control still needs to be investigated for creating smart soft agents.

This thesis aims to enable soft pneumatic robots with high-level multimodal perception with embedded internal sensing methods. To this end, the deformation-based sensing scheme for soft pneumatic robots is proposed using discrepancy characteristics between the heterogeneous sensing states of the soft actuator when stimuli are exerted. Based on this sensing method, both internal state perception (proprioception) and external interaction estimation (exteroception) are implemented on soft pneumatic robotic systems with different types and various degrees of freedom (DOFs). Then, the multimodal sensing scheme is implemented in the 3-DOF soft gripper and 3-DOF soft parallel joint. They are driven by self-sensing soft pneumatic actuators with origami structures to achieve the one-dimensional and three-dimensional position and force perception, respectively. These self-sensing soft actuators integrate two sensors for the heterogeneous modalities: a pressure sensor to measure the internal pressure of the actuator and a displacement sensor to measure the primary deformation of the actuator. Finally, a high-dimensional soft modularized robotic platform consisting of a 1-DOF soft gripper and a three-segment soft manipulator (connecting the three soft parallel joints serially), driven by self-sensing soft origami actuators. And a robust networked proprioception scheme allowing for sensor failure explored for the soft manipulator to to investigate the closed-loop control, validated by the three-dimensional position control applications. 

The presented conceptual, methodological, and experimental efforts can be extended to other large families of intelligent soft pneumatic robots. They may also be fully leveraged in various real-world tasks involving exploring unstructured environments and human interactive collaborations.

(An abstract of 474 words)
 

Multimodal Perception Deformation-based Compact Intelligent Soft Robots Closed-loop Control

英语

联合培养

2020

2024-11

[1] O. Yasa, Y. Toshimitsu, M. Y. Michelis, L. S. Jones, M. Filippi, T. Buchner, andR. K. Katzschmann. “An Overview of Soft Robotics”. In: Annual Review of Control,Robotics, and Autonomous Systems 6 (2023), pp. 1–29.
[2] C. Lee, M. Kim, Y. J. Kim, N. Hong, S. Ryu, H. J. Kim, and S. Kim. “Soft robotreview”. In: International Journal of Control, Automation and Systems 15 (2017),pp. 3–15.
[3] C. Majidi. “Soft robotics: a perspective—current trends and prospects for thefuture”. In: Soft robotics 1.1 (2014), pp. 5–11.
[4] J.Wang and A. Chortos. “Control strategies for soft robot systems”. In: AdvancedIntelligent Systems 4.5 (2022), p. 2100165.
[5] P. Polygerinos, N. Correll, S. A. Morin, B. Mosadegh, C. D. Onal, K. Petersen,M. Cianchetti, M. T. Tolley, and R. F. Shepherd. “Soft robotics: Review of fluiddrivenintrinsically soft devices; manufacturing, sensing, control, and applicationsin human-robot interaction”. In: Advanced Engineering Materials 19.12(2017), p. 1700016.
[6] Y. Liu, J. Hou, C. Li, and X. Wang. “Intelligent soft robotic grippers for agriculturaland food product handling: A brief review with a focus on design andcontrol”. In: Advanced Intelligent Systems 5.12 (2023), p. 2300233.
[7] W. Ruotolo, D. Brouwer, and M. R. Cutkosky. “From grasping to manipulationwith gecko-inspired adhesives on a multifinger gripper”. In: Science Robotics 6.61(2021), eabi9773.
[8] Y. Zhang, P. Li, J. Quan, L. Li, G. Zhang, and D. Zhou. “Progress, challenges, andprospects of soft robotics for space applications”. In: Advanced Intelligent Systems5.3 (2023), p. 2200071.
[9] R. K. Katzschmann, J. DelPreto, R. MacCurdy, and D. Rus. “Exploration of underwaterlife with an acoustically controlled soft robotic fish”. In: Science Robotics3.16 (2018), eaar3449.
[10] G. Li, X. Chen, F. Zhou, Y. Liang, Y. Xiao, X. Cao, Z. Zhang, M. Zhang, B. Wu, S.Yin, et al. “Self-powered soft robot in the Mariana Trench”. In: Nature 591.7848(2021), pp. 66–71.
[11] Z. Xie, F. Yuan, J. Liu, L. Tian, B. Chen, Z. Fu, S. Mao, T. Jin, Y. Wang, X. He,et al. “Octopus-inspired sensorized soft arm for environmental interaction”. In:Science Robotics 8.84 (2023), eadh7852.
[12] W. Liu, Y. Duo, J. Liu, F. Yuan, L. Li, L. Li, G. Wang, B. Chen, S. Wang, H. Yang,et al. “Touchless interactive teaching of soft robots through flexible bimodal sensoryinterfaces”. In: Nature communications 13.1 (2022), p. 5030.
[13] C. D. Onal, R. J. Wood, and D. Rus. “An origami-inspired approach to wormrobots”. In: IEEE/ASME Transactions on Mechatronics 18.2 (2012), pp. 430–438.
[14] D. Drotman, S. Jadhav, D. Sharp, C. Chan, and M. T. Tolley. “Electronics-freepneumatic circuits for controlling soft-legged robots”. In: Science Robotics 6.51(2021), eaay2627.
[15] P. Polygerinos, Z. Wang, K. C. Galloway, R. J. Wood, and C. J. Walsh. “Softrobotic glove for combined assistance and at-home rehabilitation”. In: Roboticsand Autonomous Systems 73 (2015), pp. 135–143.
[16] T. Proietti, C. O’Neill, L. Gerez, T. Cole, S. Mendelowitz, K. Nuckols, C. Hohimer,D. Lin, S. Paganoni, and C. Walsh. “Restoring arm function with a softrobotic wearable for individuals with amyotrophic lateral sclerosis”. In: ScienceTranslational Medicine 15.681 (2023), eadd1504.
[17] H.Wang, R. Zhang,W. Chen, X.Wang, and R. Pfeifer. “A cable-driven soft robotsurgical system for cardiothoracic endoscopic surgery: preclinical tests in animals”.In: Surgical endoscopy 31 (2017), pp. 3152–3158.
[18] H.Wang, M. Totaro, and L. Beccai. “Toward perceptive soft robots: Progress andchallenges”. In: Advanced Science 5.9 (2018), p. 1800541.
[19] C. Laschi, J. Rossiter, F. Iida, M. Cianchetti, and L. Margheri. Soft robotics: trends,applications and challenges. Vol. 17. Springer, 2017.
[20] Z. Q. Tang, H. L. Heung, K. Y. Tong, and Z. Li. “Model-based online learningand adaptive control for a “human-wearable soft robot” integrated system”. In:The International Journal of Robotics Research 40.1 (2021), pp. 256–276.
[21] W. Zhu, C. Lu, Q. Zheng, Z. Fang, H. Che, K. Tang, M. Zhu, S. Liu, and Z.Wang.“A soft-rigid hybrid gripper with lateral compliance and dexterous in-hand manipulation”.In: IEEE/ASME Transactions on Mechatronics 28.1 (2022), pp. 104–115.
[22] Z. Gong, X. Fang, X. Chen, J. Cheng, Z. Xie, J. Liu, B. Chen, H. Yang, S. Kong, Y.Hao, et al. “A soft manipulator for efficient delicate grasping in shallow water:Modeling, control, and real-world experiments”. In: The International Journal ofRobotics Research 40.1 (2021), pp. 449–469.
[23] K. Tang, C. Lu, Y. Chen, Y. Xiao, S.Wu, S. Tang, H.Wang, B. Zhang, Z. Shen, J. Yi,et al. “A Strong Underwater Soft Manipulator with Planarly-bundled Actuatorsand Accurate Position Control”. In: IEEE Robotics and Automation Letters (2023).
[24] O. Faris, R. Muthusamy, F. Renda, I. Hussain, D. Gan, L. Seneviratne, and Y.Zweiri. “Proprioception and exteroception of a soft robotic finger using neuromorphicvision-based sensing”. In: Soft Robotics 10.3 (2023), pp. 467–481.
[25] H. I. Christensen and G. D. Hager. “Sensing and estimation”. In: Springer Handbookof Robotics (2016), pp. 91–112.
[26] L.Wang, J. Lam, X. Chen, J. Li, R. Zhang, Y. Su, and Z.Wang. “Soft robot proprioceptionusing unified soft body encoding and recurrent neural network”. In:Soft Robotics 10.4 (2023), pp. 825–837.
[27] M. Yang, L. P. Cooper, N. Liu, X. Wang, and M. P. Fok. “Twining plant inspiredpneumatic soft robotic spiral gripper with a fiber optic twisting sensor”. In: Opticsexpress 28.23 (2020), pp. 35158–35167.
[28] R. Benvenuto, S. Salvi, and M. Lavagna. “Dynamics analysis and GNC designof flexible systems for space debris active removal”. In: Acta Astronautica 110(2015), pp. 247–265.
[29] H. Zhong, Z. Shen, Y. Zhao, K. Tang, W. Wang, and Z. Wang. “A hybrid underwatermanipulator system with intuitive muscle-level sEMG mapping control”.In: IEEE Robotics and Automation Letters 5.2 (2020), pp. 3198–3205.
[30] R.Wang, S.Wang, S. Du, E. Xiao,W. Yuan, and C. Feng. “Real-time soft body 3Dproprioception via deep vision-based sensing”. In: IEEE Robotics and AutomationLetters 5.2 (2020), pp. 3382–3389.
[31] T. G. Thuruthel, B. Shih, C. Laschi, and M. T. Tolley. “Soft robot perception usingembedded soft sensors and recurrent neural networks”. In: Science Robotics 4.26(2019), eaav1488.
[32] S. Sankar, D. Balamurugan, A. Brown, K. Ding, X. Xu, J. H. Low, C. H. Yeow,and N. Thakor. “Texture discrimination with a soft biomimetic finger using aflexible neuromorphic tactile sensor array that provides sensory feedback”. In:Soft Robotics 8.5 (2021), pp. 577–587.
[33] L. Scimeca, J. Hughes, P. Maiolino, and F. Iida. “Model-free soft-structure reconstructionfor proprioception using tactile arrays”. In: IEEE Robotics and AutomationLetters 4.3 (2019), pp. 2479–2484.
[34] M. Cai, Z. Jiao, S. Nie, C. Wang, J. Zou, and J. Song. “A multifunctional electronicskin based on patterned metal films for tactile sensing with a broad linearresponse range”. In: Science Advances 7.52 (2021), eabl8313.
[35] J. Huang, J. Zhou, Z.Wang, J. Law, H. Cao, Y. Li, H.Wang, and Y. Liu. “Modularorigami soft robot with the perception of interaction force and body configuration”.In: Advanced Intelligent Systems 4.9 (2022), p. 2200081.
[36] X. Huang, K. Kumar, M. K. Jawed, A. Mohammadi Nasab, Z. Ye, W. Shan, andC. Majidi. “Highly dynamic shape memory alloy actuator for fast moving softrobots”. In: Advanced Materials Technologies 4.4 (2019), p. 1800540.
[37] X. Huang, K. Kumar, M. K. Jawed, A. M. Nasab, Z. Ye, W. Shan, and C. Majidi.“Chasing biomimetic locomotion speeds: Creating untethered soft robots withshape memory alloy actuators”. In: Science Robotics 3.25 (2018), eaau7557.
[38] B. Shih, D. Shah, J. Li, T. G. Thuruthel, Y.-L. Park, F. Iida, Z. Bao, R. Kramer-Bottiglio, and M. T. Tolley. “Electronic skins and machine learning for intelligentsoft robots”. In: Science Robotics 5.41 (2020), eaaz9239.
[39] Q. Li, O. Kroemer, Z. Su, F. F. Veiga, M. Kaboli, and H. J. Ritter. “A review oftactile information: Perception and action through touch”. In: IEEE Transactionson Robotics 36.6 (2020), pp. 1619–1634.
[40] L. Wang and Z. Wang. “Mechanoreception for soft robots via intuitive bodycues”. In: Soft robotics 7.2 (2020), pp. 198–217.
[41] Y. Su, X. Chen, Z. Fang, D. Liu, J. Lam, and Z. Wang. “Spatial Position–ForcePerception for a Soft Parallel Joint via Pressure-Deformation Self-Sensing”. In:IEEE/ASME Transactions on Mechatronics (2024).
[42] J. Y. Loo, Z. Y. Ding, V. M. Baskaran, S. G. Nurzaman, and C. P. Tan. “Robust multimodalindirect sensing for soft robots via neural network-aided filter-basedestimation”. In: Soft Robotics 9.3 (2022), pp. 591–612.
[43] T. Kim, S. Lee, T. Hong, G. Shin, T. Kim, and Y.-L. Park. “Heterogeneous sensingin a multifunctional soft sensor for human-robot interfaces”. In: Science robotics5.49 (2020), eabc6878.
[44] C. Hegde, J. Su, J. M. R. Tan, K. He, X. Chen, and S. Magdassi. “Sensing in softrobotics”. In: ACS nano 17.16 (2023), pp. 15277–15307.
[45] L.Wang. “Soft pneumatic robots with bodily awareness”. In: HKU Theses Online(HKUTO) (2021).
[46] A. Koivikko, V. Lampinen, M. Pihlajamäki, K. Yiannacou, V. Sharma, and V.Sariola. “Integrated stretchable pneumatic strain gauges for electronics-free softrobots”. In: Communications Engineering 1.1 (2022), p. 14.
[47] S. C. Ryu and P. E. Dupont. “FBG-based shape sensing tubes for continuumrobots”. In: 2014 IEEE International Conference on Robotics and Automation (ICRA).IEEE. 2014, pp. 3531–3537.
[48] T. Baaij, M. K. Holkenborg, M. Stölzle, D. van der Tuin, J. Naaktgeboren, R.Babuška, and C. Della Santina. “Learning 3D shape proprioception for continuumsoft robots with multiple magnetic sensors”. In: Soft Matter 19.1 (2023),pp. 44–56.
[49] D. Bruder, X. Fu, R. B. Gillespie, C. D. Remy, and R. Vasudevan. “Koopmanbasedcontrol of a soft continuum manipulator under variable loading conditions”.In: IEEE Robotics and Automation Letters 6.4 (2021), pp. 6852–6859.
[50] R. P. Rocha, P. A. Lopes, A. T. De Almeida, M. Tavakoli, and C. Majidi. “Fabricationand characterization of bending and pressure sensors for a soft prosthetichand”. In: Journal of Micromechanics and Microengineering 28.3 (2018), p. 034001.
[51] L. Wunsch, C. G. Tenorio, K. Anding, A. Golomoz, and G. Notni. “Data Fusionof RGB and Depth Data with Image Enhancement”. In: Journal of Imaging 10.3(2024), p. 73.
[52] N. Feng, Q. Shi, H. Wang, J. Gong, C. Liu, and Z. Lu. “A soft robotic hand:design, analysis, sEMG control, and experiment”. In: The International Journal ofAdvanced Manufacturing Technology 97 (2018), pp. 319–333.
[53] H. Jiang, Z. Wang, Y. Jin, X. Chen, P. Li, Y. Gan, S. Lin, and X. Chen. “Hierarchicalcontrol of soft manipulators towards unstructured interactions”. In: TheInternational Journal of Robotics Research 40.1 (2021), pp. 411–434.
[54] J. M. Bern, Y. Schnider, P. Banzet, N. Kumar, and S. Coros. “Soft robot controlwith a learned differentiable model”. In: 2020 3rd IEEE International Conferenceon Soft Robotics (RoboSoft). IEEE. 2020, pp. 417–423.
[55] Y. Mengüç, Y.-L. Park, H. Pei, D. Vogt, P. M. Aubin, E. Winchell, L. Fluke, L.Stirling, R. J. Wood, and C. J. Walsh. “Wearable soft sensing suit for humangait measurement”. In: The International Journal of Robotics Research 33.14 (2014),pp. 1748–1764.
[56] S. Ku, B.-H. Song, T. Park, Y. Lee, and Y.-L. Park. “Soft modularized robotic armfor safe human–robot interaction based on visual and proprioceptive feedback”.In: The International Journal of Robotics Research (2024), p. 02783649241227249.
[57] I. Huang, J. Liu, and R. Bajcsy. “A depth camera-based soft fingertip device forcontact region estimation and perception-action coupling”. In: 2019 InternationalConference on Robotics and Automation (ICRA). IEEE. 2019, pp. 8443–8449.
[58] Y. Xu and Z. Wang. “Visual sensing technologies in robotic welding: Recent researchdevelopments and future interests”. In: Sensors and Actuators A: Physical320 (2021), p. 112551.
[59] J. Lai, B. Lu, and H. K. Chu. “Variable-stiffness control of a dual-segment softrobot using depth vision”. In: IEEE/ASME Transactions on Mechatronics 27.2 (2021),pp. 1034–1045.
[60] B. Mazzolai, T. Kraus, N. Pirrone, L. Kooistra, A. De Simone, A. Cottin, andL. Margheri. “Towards new frontiers for distributed environmental monitoringbased on an ecosystem of plant seed-like soft robots”. In: Proceedings of the Conferenceon Information Technology for Social Good. 2021, pp. 221–224.
[61] J.-T. Lin, C. A. Newquist, and C. K. Harnett. “Multitouch pressure sensing withsoft optical time-of-flight sensors”. In: IEEE transactions on instrumentation andmeasurement 71 (2022), pp. 1–8.
[62] Q. Shi, Z. Sun, X. Le, J. Xie, and C. Lee. “Soft robotic perception system withultrasonic auto-positioning and multimodal sensory intelligence”. In: ACS nano17.5 (2023), pp. 4985–4998.
[63] R. Yasin and N. Simaan. “Joint-level force sensing for indirect hybrid force/positioncontrol of continuum robots with friction”. In: The International Journal of RoboticsResearch 40.4-5 (2021), pp. 764–781.
[64] W. Xu, H. Zhang, H. Yuan, and B. Liang. “A compliant adaptive gripper andits intrinsic force sensing method”. In: IEEE Transactions on Robotics 37.5 (2021),pp. 1584–1603.
[65] S. E. Navarro, S. Nagels, H. Alagi, L.-M. Faller, O. Goury, T. Morales-Bieze, H.Zangl, B. Hein, R. Ramakers, W. Deferme, et al. “A model-based sensor fusionapproach for force and shape estimation in soft robotics”. In: IEEE Robotics andAutomation Letters 5.4 (2020), pp. 5621–5628.
[66] R. Hashem, M. Stommel, L. K. Cheng, andW. Xu. “Design and characterizationof a bellows-driven soft pneumatic actuator”. In: IEEE/ASME Transactions onMechatronics 26.5 (2020), pp. 2327–2338.
[67] S. Ozel, N. A. Keskin, D. Khea, and C. D. Onal. “A precise embedded curvaturesensor module for soft-bodied robots”. In: Sensors and Actuators A: Physical 236(2015), pp. 349–356.
[68] J. Byun, Y. Lee, J. Yoon, B. Lee, E. Oh, S. Chung, T. Lee, K.-J. Cho, J. Kim, andY. Hong. “Electronic skins for soft, compact, reversible assembly of wirelesslyactivated fully soft robots”. In: Science robotics 3.18 (2018), eaas9020.
[69] K. Kure, T. Kanda, K. Suzumori, and S. Wakimoto. “Flexible displacement sensorusing injected conductive paste”. In: Sensors and Actuators A: Physical 143.2(2008), pp. 272–278.
[70] W. Felt, K. Y. Chin, and C. D. Remy. “Contraction sensing with smart braidMcKibben muscles”. In: IEEE/ASME Transactions on Mechatronics 21.3 (2015),pp. 1201–1209.
[71] M. Luo, E. H. Skorina,W. Tao, F. Chen, S. Ozel, Y. Sun, and C. D. Onal. “Towardmodular soft robotics: Proprioceptive curvature sensing and sliding-mode controlof soft bidirectional bending modules”. In: Soft robotics 4.2 (2017), pp. 117–125.
[72] M. Park, Y. Ohm, D. Kim, and Y.-L. Park. “Multi-material soft strain sensorswith high gauge factors for proprioceptive sensing of soft bending actuators”.In: 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft). IEEE. 2019,pp. 384–390.
[73] X. Yan, C. R. Bowen, C. Yuan, Z. Hao, and M. Pan. “Carbon fibre based flexiblepiezoresistive composites to empower inherent sensing capabilities for softactuators”. In: Soft Matter 15.40 (2019), pp. 8001–8011.
[74] R. B. Scharff, R. M. Doornbusch, E. L. Doubrovski, J. Wu, J. M. Geraedts, andC. C. Wang. “Color-based proprioception of soft actuators interacting with objects”.In: IEEE/ASME Transactions on Mechatronics 24.5 (2019), pp. 1964–1973.
[75] K. C. Galloway, Y. Chen, E. Templeton, B. Rife, I. S. Godage, and E. J. Barth.“Fiber optic shape sensing for soft robotics”. In: Soft robotics 6.5 (2019), pp. 671–684.
[76] Z. Wang, P. Polygerinos, J. T. Overvelde, K. C. Galloway, K. Bertoldi, and C. J.Walsh. “Interaction forces of soft fiber reinforced bending actuators”. In: IEEE/ASMETransactions on Mechatronics 22.2 (2016), pp. 717–727.
[77] T. Hellebrekers, N. Chang, K. Chin, M. J. Ford, O. Kroemer, and C. Majidi. “Softmagnetic tactile skin for continuous force and location estimation using neuralnetworks”. In: IEEE Robotics and Automation Letters 5.3 (2020), pp. 3892–3898.
[78] R. B. Scharff, G. Fang, Y. Tian, J. Wu, J. M. Geraedts, and C. C. Wang. “Sensingand reconstruction of 3-d deformation on pneumatic soft robots”. In: IEEE/ASMETransactions on Mechatronics 26.4 (2021), pp. 1877–1885.
[79] S. Sareh, Y. Noh, M. Li, T. Ranzani, H. Liu, and K. Althoefer. “Macrobend opticalsensing for pose measurement in soft robot arms”. In: Smart Materials andStructures 24.12 (2015), p. 125024.
[80] M. Cianchetti, F. Renda, A. Licofonte, and C. Laschi. “Sensorization of continuumsoft robots for reconstructing their spatial configuration”. In: 2012 4th IEEERAS & EMBS International Conference on Biomedical Robotics and Biomechatronics(BioRob). IEEE. 2012, pp. 634–639.
[81] V.Wall, G. Zöller, and O. Brock. “A method for sensorizing soft actuators and itsapplication to the RBO hand 2”. In: 2017 IEEE International Conference on Roboticsand Automation (ICRA). IEEE. 2017, pp. 4965–4970.
[82] Z. Fang, C. Huang, Y. Wang, J. Xu, J. Tan, B. Li, Z. Wang, Y. Wu, A. Huang, J.Yi, et al. “Multi-dimensional proprioception and stiffness tuning for soft roboticjoints”. In: 2022 International Conference on Robotics and Automation (ICRA). IEEE.2022, pp. 10973–10979.
[83] W. Ouyang, L. He, A. Albini, and P. Maiolino. “A modular soft robotic armwith embedded tactile sensors for proprioception”. In: 2022 IEEE 5th InternationalConference on Soft Robotics (RoboSoft). IEEE. 2022, pp. 919–924.
[84] R. L. Truby, C. Della Santina, and D. Rus. “Distributed proprioception of 3Dconfiguration in soft, sensorized robots via deep learning”. In: IEEE Robotics andAutomation Letters 5.2 (2020), pp. 3299–3306.
[85] W. Felt, M. J. Telleria, T. F. Allen, G. Hein, J. B. Pompa, K. Albert, and C. D. Remy.“An inductance-based sensing system for bellows-driven continuum joints insoft robots”. In: Autonomous robots 43 (2019), pp. 435–448.
[86] K. S. Kumar, X. Xiao, M. S. Kalairaj, G. Ponraj, C. Li, C. J. Cai, C. M. Lim,and H. Ren. “Omnidirectional steerable forceps with flexible joints and skinlikestretchable strain sensors”. In: IEEE/ASME Transactions on Mechatronics 27.2(2021), pp. 713–724.
[87] K. Elgeneidy, G. Neumann, S. Pearson, M. Jackson, and N. Lohse. “Contact detectionand size estimation using a modular soft gripper with embedded flexsensors”. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS). IEEE. 2018, pp. 498–503.
[88] S. Mikogai, B. Kazumi, and K. Takemura. “Contact point estimation along airtube based on acoustic sensing of pneumatic system noise”. In: IEEE Roboticsand Automation Letters 5.3 (2020), pp. 4618–4625.
[89] H. Yang, Y. Chen, Y. Sun, and L. Hao. “A novel pneumatic soft sensor for measuringcontact force and curvature of a soft gripper”. In: Sensors and Actuators A:Physical 266 (2017), pp. 318–327.
[90] H. Mun, D. S. Diaz Cortes, J.-H. Youn, and K.-U. Kyung. “Multi-Degree-of-Freedom Force Sensor Incorporated into Soft Robotic Gripper for ImprovedGrasping Stability”. In: Soft Robotics (2024).
[91] H. Zhang and M. Y. Wang. “Multi-axis soft sensors based on dielectric elastomer”.In: Soft Robotics 3.1 (2016), pp. 3–12.
[92] S. Sundaram, P. Kellnhofer, Y. Li, J.-Y. Zhu, A. Torralba, andW. Matusik. “Learningthe signatures of the human grasp using a scalable tactile glove”. In: Nature569.7758 (2019), pp. 698–702.
[93] A. Llamosi and S. Toussaint. “Measuring force intensity and direction with aspatially resolved soft sensor for biomechanics and robotic haptic capability”.In: Soft robotics 6.3 (2019), pp. 346–355.
[94] Y. Wang, J. Chen, and D. Mei. “Flexible tactile sensor array for slippage andgrooved surface recognition in sliding movement”. In: Micromachines 10.9 (2019),p. 579.
[95] M. Amjadi, K.-U. Kyung, I. Park, and M. Sitti. “Stretchable, skin-mountable, andwearable strain sensors and their potential applications: a review”. In: AdvancedFunctional Materials 26.11 (2016), pp. 1678–1698.
[96] P. Rothemund, A. Ainla, L. Belding, D. J. Preston, S. Kurihara, Z. Suo, and G. M.Whitesides. “A soft, bistable valve for autonomous control of soft actuators”. In:Science Robotics 3.16 (2018), eaar7986.
[97] T. George Thuruthel, P. Gardner, and F. Iida. “Closing the control loop withtime-variant embedded soft sensors and recurrent neural networks”. In: SoftRobotics 9.6 (2022), pp. 1167–1176.
[98] W.-Y. Li, A. Takata, H. Nabae, G. Endo, and K. Suzumori. “Shape recognitionof a tensegrity with soft sensor threads and artificial muscles using a recurrentneural network”. In: IEEE Robotics and Automation Letters 6.4 (2021), pp. 6228–6234.
[99] W. Sun, N. Akashi, Y. Kuniyoshi, and K. Nakajima. “Physics-informed recurrentneural networks for soft pneumatic actuators”. In: IEEE Robotics and AutomationLetters 7.3 (2022), pp. 6862–6869.
[100] I. Van Meerbeek, C. De Sa, and R. Shepherd. “Soft optoelectronic sensory foamswith proprioception”. In: Science Robotics 3.24 (2018), eaau2489.
[101] Z. Wang and S. Hirai. “Soft gripper dynamics using a line-segment model withan optimization-based parameter identification method”. In: IEEE Robotics andAutomation Letters 2.2 (2017), pp. 624–631.
[102] D. Bruder, X. Fu, R. B. Gillespie, C. D. Remy, and R. Vasudevan. “Data-drivencontrol of soft robots using Koopman operator theory”. In: IEEE Transactions onRobotics 37.3 (2020), pp. 948–961.
[103] K. Elgeneidy, N. Lohse, and M. Jackson. “Bending angle prediction and controlof soft pneumatic actuators with embedded flex sensors–a data-driven approach”.In: Mechatronics 50 (2018), pp. 234–247.
[104] J. Zimmer, T. Hellebrekers, T. Asfour, C. Majidi, and O. Kroemer. “Predictinggrasp success with a soft sensing skin and shape-memory actuated gripper”. In:2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE. 2019, pp. 7120–7127.
[105] J. Li, X. Chen, Y. Su, W. Wang, J. Lam, and Z. Wang. “Kinematic analysis ofsoft continuum manipulators based on sparse workspace mapping”. In: IEEERobotics and Automation Letters 7.2 (2022), pp. 5055–5062.
[106] E. Coevoet, A. Escande, and C. Duriez. “Soft robots locomotion and manipulationcontrol using FEM simulation and quadratic programming”. In: 2019 2ndIEEE International Conference on Soft Robotics (RoboSoft). IEEE. 2019, pp. 739–745.
[107] M. W. Hannan and I. D. Walker. “Kinematics and the implementation of anelephant’s trunk manipulator and other continuum style robots”. In: Journal ofrobotic systems 20.2 (2003), pp. 45–63.
[108] B. A. Jones and I. D. Walker. “Kinematics for multisection continuum robots”.In: IEEE Transactions on Robotics 22.1 (2006), pp. 43–55.
[109] M. Rolf and J. J. Steil. “Constant curvature continuum kinematics as fast approximatemodel for the Bionic Handling Assistant”. In: 2012 IEEE/RSJ InternationalConference on Intelligent Robots and Systems. IEEE. 2012, pp. 3440–3446.
[110] Y. J. Martin, D. Bruder, and R. J.Wood. “A proprioceptive method for soft robotsusing inertial measurement units”. In: 2022 IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS). IEEE. 2022, pp. 9379–9384.
[111] M. Park, B. Jeong, and Y.-L. Park. “Hybrid system analysis and control of a softrobotic gripper with embedded proprioceptive sensing for enhanced grippingperformance”. In: Advanced Intelligent Systems 3.3 (2021), p. 2000061.
[112] X. Huang, J. Zou, and G. Gu. “Kinematic modeling and control of variable curvaturesoft continuum robots”. In: IEEE/ASME Transactions on Mechatronics 26.6(2021), pp. 3175–3185.
[113] T. Mahl, A. Hildebrandt, and O. Sawodny. “A variable curvature continuumkinematics for kinematic control of the bionic handling assistant”. In: IEEE transactionson robotics 30.4 (2014), pp. 935–949.
[114] T. Mahl, A. E. Mayer, A. Hildebrandt, and O. Sawodny. “A variable curvaturemodeling approach for kinematic control of continuum manipulators”. In: 2013American Control Conference. IEEE. 2013, pp. 4945–4950.
[115] M. Tummers, V. Lebastard, F. Boyer, J. Troccaz, B. Rosa, and M. T. Chikhaoui.“Cosserat rod modeling of continuum robots from newtonian and lagrangianperspectives”. In: IEEE Transactions on Robotics (2023).
[116] A. Hooshiar, A. Sayadi, M. Jolaei, and J. Dargahi. “Accurate estimation of tipforce on tendon-driven catheters using inverse cosserat rod model”. In: 2020 InternationalConference on Biomedical Innovations and Applications (BIA). IEEE. 2020,pp. 37–40.
[117] P. Polygerinos, Z.Wang, J. T. Overvelde, K. C. Galloway, R. J.Wood, K. Bertoldi,and C. J. Walsh. “Modeling of soft fiber-reinforced bending actuators”. In: IEEETransactions on Robotics 31.3 (2015), pp. 778–789.
[118] M. Yousefi, M. Jamshidian Ghaleshahi,H. Nejat Pishkenari, and A. Alasty. “Modelaided3D shape and force estimation of continuum robots based on Cosserat rodtheory and using a magnetic localization system”. In: Intelligent Service Robotics16.4 (2023), pp. 471–484.
[119] S. Haddadin, A. De Luca, and A. Albu-Schäffer. “Robot collisions: A surveyon detection, isolation, and identification”. In: IEEE Transactions on Robotics 33.6(2017), pp. 1292–1312.
[120] A. De Luca and R. Mattone. “Sensorless robot collision detection and hybridforce/motion control”. In: Proceedings of the 2005 IEEE international conference onrobotics and automation. IEEE. 2005, pp. 999–1004.
[121] A. De Luca and R. Mattone. “Actuator failure detection and isolation using generalizedmomenta”. In: 2003 IEEE international conference on robotics and automation(cat. No. 03CH37422). Vol. 1. IEEE. 2003, pp. 634–639.
[122] J. Ham, A. K. Han, M. R. Cutkosky, and Z. Bao. “UV-laser-machined stretchablemulti-modal sensor network for soft robot interaction”. In: npj Flexible Electronics6.1 (2022), p. 94.
[123] R. L. Truby, M. Wehner, A. K. Grosskopf, D. M. Vogt, S. G. Uzel, R. J. Wood,and J. A. Lewis. “Soft somatosensitive actuators via embedded 3D printing”. In:Advanced Materials 30.15 (2018), p. 1706383.
[124] Q. Hua, J. Sun, H. Liu, R. Bao, R. Yu, J. Zhai, C. Pan, and Z. L. Wang. “Skininspiredhighly stretchable and conformable matrix networks for multifunctionalsensing”. In: Nature communications 9.1 (2018), p. 244.
[125] S. M. Won, H. Wang, B. H. Kim, K. Lee, H. Jang, K. Kwon, M. Han, K. E. Crawford,H. Li, Y. Lee, et al. “Multimodal sensing with a three-dimensional piezoresistivestructure”. In: ACS nano 13.10 (2019), pp. 10972–10979.
[126] Y.-C. Lai, J. Deng, R. Liu, Y.-C. Hsiao, S. L. Zhang,W. Peng, H.-M.Wu, X.Wang,and Z. L. Wang. “Actively perceiving and responsive soft robots enabled byself-powered, highly extensible, and highly sensitive triboelectric proximityandpressure-sensing skins”. In: Advanced Materials 30.28 (2018), p. 1801114.
[127] J. Di, Z. Dugonjic, W. Fu, T. Wu, R. Mercado, K. Sawyer, V. R. Most, G. Kammerer,S. Speidel, R. E. Fan, et al. “Using Fiber Optic Bundles to MiniaturizeVision-Based Tactile Sensors”. In: arXiv preprint arXiv:2403.05500 (2024).
[128] G. Soter, A. Conn, H. Hauser, and J. Rossiter. “Bodily aware soft robots: integrationof proprioceptive and exteroceptive sensors”. In: 2018 IEEE internationalconference on robotics and automation (ICRA). IEEE. 2018, pp. 2448–2453.
[129] X. Chen, D. Duanmu, and Z. Wang. “Model-based control and external loadestimation of an extensible soft robotic arm”. In: Frontiers in Robotics and AI 7(2021), p. 586490.
[130] Z. Sun, M. Zhu, Z. Zhang, Z. Chen, Q. Shi, X. Shan, R. C. H. Yeow, and C. Lee.“Artificial Intelligence of Things (AIoT) enabled virtual shop applications usingself-powered sensor enhanced soft robotic manipulator”. In: Advanced Science8.14 (2021), p. 2100230.
[131] H. Zhao, K. O’brien, S. Li, and R. F. Shepherd. “Optoelectronically innervatedsoft prosthetic hand via stretchable optical waveguides”. In: Science robotics 1.1(2016), eaai7529.
[132] C. Tawk, E. Sariyildiz, and G. Alici. “Force control of a 3D printed soft gripperwith built-in pneumatic touch sensing chambers”. In: Soft Robotics 9.5 (2022),pp. 970–980.
[133] C. Tawk, H. Zhou, E. Sariyildiz, M. In Het Panhuis, G. M. Spinks, and G. Alici.“Design, modeling, and control of a 3D printed monolithic soft robotic fingerwith embedded pneumatic sensing chambers”. In: IEEE/ASME Transactions onMechatronics 26.2 (2020), pp. 876–887.
[134] L. Wang, Q. Li, J. Lam, and Z. Wang. “Tactual recognition of soft objects fromdeformation cues”. In: IEEE Robotics and Automation Letters 7.1 (2021), pp. 96–103.
[135] H. R. Choi, K. Jung, S. Ryew, J.-D. Nam, J. Jeon, J. C. Koo, and K. Tanie. “Biomimeticsoft actuator: design, modeling, control, and applications”. In: IEEE/ASME transactionson mechatronics 10.5 (2005), pp. 581–593.
[136] P. Abbasi, M. A. Nekoui, M. Zareinejad, P. Abbasi, and Z. Azhang. “Position andforce control of a soft pneumatic actuator”. In: Soft Robotics 7.5 (2020), pp. 550–563.
[137] M. H. Mohamed, S. H.Wagdy, M. A. Atalla, A. Rehan Youssef, and S. A. Maged.“A proposed soft pneumatic actuator control based on angle estimation fromdata-driven model”. In: Proceedings of the Institution of Mechanical Engineers, PartH: Journal of Engineering in Medicine 234.6 (2020), pp. 612–625.
[138] M. O.Williams, I. G. Kevrekidis, and C.W. Rowley. “A data–driven approximationof the koopman operator: Extending dynamic mode decomposition”. In:Journal of Nonlinear Science 25 (2015), pp. 1307–1346.
[139] H. Jiang, Z. Wang, Y. Jin, X. Chen, P. Li, Y. Gan, S. Lin, and X. Chen. “Hierarchicalcontrol of soft manipulators towards unstructured interactions”. In: TheInternational Journal of Robotics Research 40.1 (2021), pp. 411–434.
[140] H. Wang, H. Ni, J. Wang, and W. Chen. “Hybrid vision/force control of softrobot based on a deformation model”. In: IEEE Transactions on Control SystemsTechnology 29.2 (2019), pp. 661–671.
[141] T. George Thuruthel, Y. Ansari, E. Falotico, and C. Laschi. “Control strategiesfor soft robotic manipulators: A survey”. In: Soft robotics 5.2 (2018), pp. 149–163.
[142] Z. Lin, Z.Wang,W. Zhao, Y. Xu, X.Wang, T. Zhang, Z. Sun, L. Lin, and Z. Peng.“Recent advances in perceptive intelligence for soft robotics”. In: Advanced IntelligentSystems 5.5 (2023), p. 2200329.
[143] T. G. Thuruthel, J. Hughes, A. Georgopoulou, F. Clemens, and F. Iida. “Usingredundant and disjoint time-variant soft robotic sensors for accurate static stateestimation”. In: IEEE Robotics and Automation Letters 6.2 (2021), pp. 2099–2105.
[144] S. Terryn, J. Brancart, D. Lefeber, G. Van Assche, and B. Vanderborght. “Selfhealingsoft pneumatic robots”. In: Science Robotics 2.9 (2017), eaan4268.
[145] Z. Tang, P. Wang, W. Xin, and C. Laschi. “Learning-based approach for a softassistive robotic arm to achieve simultaneous position and force control”. In:IEEE Robotics and Automation Letters 7.3 (2022), pp. 8315–8322.
[146] G. Gerboni, A. Diodato, G. Ciuti, M. Cianchetti, and A. Menciassi. “Feedbackcontrol of soft robot actuators via commercial flex bend sensors”. In: IEEE/ASMETransactions on Mechatronics 22.4 (2017), pp. 1881–1888.
[147] H. Ju, B. Cha, D. Rus, and J. Lee. “Closed-Loop Soft Robot Control Frameworkswith Coordinated Policies Based on Reinforcement Learning and ProprioceptiveSelf-Sensing”. In: Advanced Functional Materials (2023), p. 2304642.
[148] R. B. Scharff, J. Wu, J. M. Geraedts, and C. C. Wang. “Reducing out-of-planedeformation of soft robotic actuators for stable grasping”. In: 2019 2nd IEEE internationalconference on soft robotics (RoboSoft). IEEE. 2019, pp. 265–270
[149] F. Connolly, C. J. Walsh, and K. Bertoldi. “Automatic design of fiber-reinforcedsoft actuators for trajectory matching”. In: Proceedings of the National Academy ofSciences 114.1 (2017), pp. 51–56.
[150] D. Rus and M. T. Tolley. “Design, fabrication and control of origami robots”. In:Nature Reviews Materials 3.6 (2018), pp. 101–112.
[151] U. Culha, S. G. Nurzaman, F. Clemens, and F. Iida. “Svas3: strain vector aidedsensorization of soft structures”. In: Sensors 14.7 (2014), pp. 12748–12770.
[152] R. L. Truby, R. K. Katzschmann, J. A. Lewis, and D. Rus. “Soft robotic fingerswith embedded ionogel sensors and discrete actuation modes for somatosensitivemanipulation”. In: 2019 2nd IEEE international conference on soft robotics(RoboSoft). IEEE. 2019, pp. 322–329.
[153] K. Chen, T. Lai, F. Yang, J. Zhang, and L. Yao. “A one-DOF compliant grippermechanism with four identical twofold-symmetric Bricard linkages”. In: Robotica41.4 (2023), pp. 1098–1114.
[154] Z. Fang, Y. Wu, Y. Su, J. Yi, S. Liu, and Z. Wang. “Omnidirectional complianceon cross-linked actuator coordination enables simultaneous multi-functions ofsoft modular robots”. In: Scientific Reports 13.1 (2023), p. 12116.
[155] R. K. Katzschmann, A. D. Marchese, and D. Rus. “Autonomous object manipulationusing a soft planar grasping manipulator”. In: Soft robotics 2.4 (2015),pp. 155–164.
[156] H. Wang, C. Wang, W. Chen, X. Liang, and Y. Liu. “Three-dimensional dynamicsfor cable-driven soft manipulator”. In: Ieee/Asme Transactions on Mechatronics22.1 (2016), pp. 18–28.
[157] J. Liu, X.Wang, S. Liu, J. Yi, X.Wang, and Z.Wang. “Vertebraic soft robotic jointdesign with twisting and antagonism”. In: IEEE Robotics and Automation Letters7.2 (2021), pp. 658–665.
[158] S. Liu, J. Liu, K. Zou, X. Wang, Z. Fang, J. Yi, and Z. Wang. “A six degreesof-freedom soft robotic joint with tilt-arranged origami actuator”. In: Journal ofMechanisms and Robotics 14.6 (2022), p. 060912.
[159] G. Olson, J. A. Adams, and Y. Mengüç. “Redundancy and overactuation in cephalopodinspiredsoft robot arms”. In: Bioinspiration & biomimetics 17.3 (2022), p. 036004.
[160] R. Mengacci, F. Angelini, M. G. Catalano, G. Grioli, A. Bicchi, and M. Garabini.“Stiffness bounds for resilient and stable physical interaction of articulated softrobots”. In: IEEE Robotics and Automation Letters 4.4 (2019), pp. 4131–4138.
[161] C. D. Onal and D. Rus. “A modular approach to soft robots”. In: 2012 4th IEEERAS & EMBS International Conference on Biomedical Robotics and Biomechatronics(BioRob). IEEE. 2012, pp. 1038–1045.
[162] D. D. Arachchige and I. S. Godage. “Hybrid soft robots incorporating soft andstiff elements”. In: 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft).IEEE. 2022, pp. 267–272.
[163] M. E. Giannaccini, C. Xiang, A. Atyabi, T. Theodoridis, S. Nefti-Meziani, andS. Davis. “Novel design of a soft lightweight pneumatic continuum robot arm with decoupled variable stiffness and positioning”. In: Soft robotics 5.1 (2018),pp. 54–70.
[164] D. Bruder, M. A. Graule, C. B. Teeple, and R. J. Wood. “Increasing the payloadcapacity of soft robot arms by localized stiffening”. In: Science Robotics 8.81(2023), eadf9001.
[165] K. Oliver-Butler, J. Till, and C. Rucker. “Continuum robot stiffness under externalloads and prescribed tendon displacements”. In: IEEE Transactions on Robotics35.2 (2019), pp. 403–419.
[166] J. Fra´s, J. Czarnowski, M. Macia´s, and J. Główka. “Static modeling of multisectionsoft continuum manipulator for stiff-flop project”. In: Recent Advances inAutomation, Robotics and Measuring Techniques. Springer. 2014, pp. 365–375.
[167] X. Huang, X. Zhu, and G. Gu. “Kinematic modeling and characterization of softparallel robots”. In: IEEE Transactions on Robotics 38.6 (2022), pp. 3792–3806.
[168] C. Tutcu, B. A. Baydere, S. K. Talas, and E. Samur. “Quasi-static modeling ofa novel growing soft-continuum robot”. In: The International Journal of RoboticsResearch 40.1 (2021), pp. 86–98.
[169] P. Ferrentino, A. López-Díaz, S. Terryn, J. Legrand, J. Brancart, G. Van Assche,E. Vázquez, A. Vázquez, and B. Vanderborght. “Quasi-static fea model for amulti-material soft pneumatic actuator in sofa”. In: IEEE Robotics and AutomationLetters 7.3 (2022), pp. 7391–7398.
[170] J. Shintake, S. Rosset, B. E. Schubert, D. Floreano, and H. Shea. “Versatile softgrippers with intrinsic electroadhesion based on multifunctional polymer actuators”.In: Advanced materials 28.2 (2016), pp. 231–238.
[171] E. D’Anna, G. Valle, A. Mazzoni, I. Strauss, F. Iberite, J. Patton, F. M. Petrini, S.Raspopovic, G. Granata, R. Di Iorio, et al. “A closed-loop hand prosthesis withsimultaneous intraneural tactile and position feedback”. In: Science Robotics 4.27(2019), eaau8892.
[172] W. Wang and S.-H. Ahn. “Shape memory alloy-based soft gripper with variablestiffness for compliant and effective grasping”. In: Soft robotics 4.4 (2017),pp. 379–389.
[173] L. Paez, G. Agarwal, and J. Paik. “Design and analysis of a soft pneumatic actuatorwith origami shell reinforcement”. In: Soft Robotics 3.3 (2016), pp. 109–119.
[174] M. S. Xavier, A. J. Fleming, and Y. K. Yong. “Design and control of pneumaticsystems for soft robotics: A simulation approach”. In: IEEE Robotics and AutomationLetters 6.3 (2021), pp. 5800–5807.
[175] J. Zhou, X. Chen, J. Li, Y. Tian, and Z. Wang. “A soft robotic approach to robustand dexterous grasping”. In: 2018 IEEE International Conference on Soft Robotics(RoboSoft). IEEE. 2018, pp. 412–417.
[176] J. Zhou, S. Chen, and Z. Wang. “A soft-robotic gripper with enhanced objectadaptation and grasping reliability”. In: IEEE Robotics and automation letters 2.4(2017), pp. 2287–2293.
[177] J. Zhou, J. Yi, X. Chen, Z. Liu, and Z. Wang. “BCL-13: A 13-DOF soft robotichand for dexterous grasping and in-hand manipulation”. In: IEEE Robotics andAutomation Letters 3.4 (2018), pp. 3379–3386.
[178] C.-H. Liu, T.-L. Chen, C.-H. Chiu, M.-C. Hsu, Y. Chen, T.-Y. Pai,W.-G. Peng, andY.-P. Chiang. “Optimal design of a soft robotic gripper for grasping unknownobjects”. In: Soft robotics 5.4 (2018), pp. 452–465.
[179] C.-H. Liu, F.-M. Chung, Y. Chen, C.-H. Chiu, and T.-L. Chen. “Optimal designof a motor-driven three-finger soft robotic gripper”. In: IEEE/ASME Transactionson Mechatronics 25.4 (2020), pp. 1830–1840.
[180] M. Liu, L. Hao,W. Zhang, and Z. Zhao. “A novel design of shape-memory alloybasedsoft robotic gripper with variable stiffness”. In: International journal of advancedrobotic systems 17.1 (2020), p. 1729881420907813.
[181] M. T. Gillespie, C. M. Best, E. C. Townsend, D. Wingate, and M. D. Killpack.“Learning nonlinear dynamic models of soft robots for model predictive controlwith neural networks”. In: 2018 IEEE International Conference on Soft Robotics(RoboSoft). IEEE. 2018, pp. 39–45.
[182] M. Grube, J. C.Wieck, and R. Seifried. “Comparison of modern control methodsfor soft robots”. In: Sensors 22.23 (2022), p. 9464.
[183] U. Proske and S. C. Gandevia. “The proprioceptive senses: their roles in signalingbody shape, body position and movement, and muscle force”. In: Physiologicalreviews (2012).
[184] K. M. Lynch and F. C. Park. Modern robotics. Cambridge University Press, 2017.
[185] K.Waldron, K.Waldron, J. Schmiedeler, and J. Schmiedeler. “Handbook of RoboticsChapter 1: Kinematics”. In: (2007).
[186] G. Alici, T. Canty, R. Mutlu,W. Hu, and V. Sencadas. “Modeling and experimentalevaluation of bending behavior of soft pneumatic actuators made of discreteactuation chambers”. In: Soft robotics 5.1 (2018), pp. 24–35.
[187] S. Neppalli, M. A. Csencsits, B. A. Jones, and I. D.Walker. “Closed-form inversekinematics for continuum manipulators”. In: Advanced Robotics 23.15 (2009), pp. 2077–2091.
[188] G. A. Naselli and B. Mazzolai. “The softness distribution index: towards the creationof guidelines for the modeling of soft-bodied robots”. In: The InternationalJournal of Robotics Research 40.1 (2021), pp. 197–223.
[189] Z. Jin, R. M. Marian, and J. S. Chahl. “A new redundancy strategy for enablinggraceful degradation in resilient robotic flexible assembly cells”. In: The InternationalJournal of Advanced Manufacturing Technology (2024), pp. 1–17.
[190] P. Bentley. “Investigations into graceful degradation of evolutionary developmentalsoftware”. In: Natural Computing 4 (2005), pp. 417–437.
[191] A. Adlemo and S.-A. Andreasson. “Improved availability in manufacturing systemsthrough graceful degradation: case study of a machining cell”. In: Proceedingsof 1995 IEEE International Conference on Robotics and Automation. Vol. 2. IEEE.1995, pp. 1744–1750.
[192] B. Shih, E. Lathrop, I. Adibnazari, R. Martin, Y.-L. Park, and M. T. Tolley. “Classificationof components of affective touch using rapidly-manufacturable soft sensorskins”. In: 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft).IEEE. 2020, pp. 182–187.
[193] C. Wilson, J. Karam, C. Votzke, F. Rozaidi, C. J. Palmer, R. L. Hatton, and M. L.Johnston. “Modular Sensor Integration into Soft Robots using StretchableWiresfor Nuclear Infrastructure Inspection and Radiation Spectroscopy”. In: 2023 IEEEInternational Conference on Soft Robotics (RoboSoft). IEEE. 2023, pp. 1–6.
[194] E. Roels, S. Terryn, J. Brancart, R. Verhelle, G. Van Assche, and B. Vanderborght.“Additive manufacturing for self-healing soft robots”. In: Soft robotics 7.6 (2020),pp. 711–723.
[195] W. Tang, C. Zhang, Y. Zhong, P. Zhu, Y. Hu, Z. Jiao, X. Wei, G. Lu, J. Wang, Y.Liang, et al. “Customizing a self-healing soft pump for robot”. In: Nature communications12.1 (2021), p. 2247.
[196] R. J. Webster III and B. A. Jones. “Design and kinematic modeling of constantcurvature continuum robots: A review”. In: The International Journal of RoboticsResearch 29.13 (2010), pp. 1661–1683.
[197] S. Liu, J. Liu, K. Zou, X. Wang, Z. Fang, J. Yi, and Z. Wang. “A six degreesof-freedom soft robotic joint with tilt-arranged origami actuator”. In: Journal ofMechanisms and Robotics 14.6 (2022), p. 060912.
[198] A. Miriyev, K. Stack, and H. Lipson. “Soft material for soft actuators”. In: Naturecommunications 8.1 (2017), p. 596.
[199] W. Chen, C. Xiong, C. Liu, P. Li, and Y. Chen. “Fabrication and dynamic modelingof bidirectional bending soft actuator integrated with optical waveguidecurvature sensor”. In: Soft robotics 6.4 (2019), pp. 495–506.
[200] Y. Li, Y. Cao, and F. Jia. “A neural network based dynamic control method forsoft pneumatic actuator with symmetrical chambers”. In: Actuators. Vol. 10. 6.MDPI. 2021, p. 112.
[201] A. Huang, Y. Cao, J. Guo, Z. Fang, Y. Su, S. Liu, J. Yi, H. Wang, J. S. Dai, and Z.Wang. “Foam-Embedded Soft Robotic Joint With Inverse Kinematic Modelingby Iterative Self-Improving Learning”. In: IEEE Robotics and Automation Letters(2024).
[202] Z. Gong, J. Cheng, K. Hu, T.Wang, and L.Wen. “An inverse kinematics methodof a soft robotic arm with three-dimensional locomotion for underwater manipulation”.In: 2018 IEEE International Conference on Soft Robotics (RoboSoft). IEEE.2018, pp. 516–521.
[203] G. Fang, Y. Tian, Z.-X. Yang, J. M. Geraedts, and C. C.Wang. “Efficient jacobianbasedinverse kinematics with sim-to-real transfer of soft robots by learning”.In: IEEE/ASME Transactions on Mechatronics 27.6 (2022), pp. 5296–5306.
[204] T. Yoon, Y. Chai, Y. Jang, H. Lee, J. Kim, J. Kwon, J. Kim, and S. Choi. “Kinematics-Informed Neural Networks: Enhancing Generalization Performance of Soft RobotModel Identification”. In: IEEE Robotics and Automation Letters (2024).
[205] C. Relaño, J. Muñoz, and C. A. Monje. “Gaussian process regression for forwardand inverse kinematics of a soft robotic arm”. In: Engineering Applications of ArtificialIntelligence 126 (2023), p. 107174.
[206] A. Malik, Y. Lischuk, T. Henderson, and R. Prazenica. “A deep reinforcementlearningapproach for inverse kinematics solution of a high degree of freedomrobotic manipulator”. In: Robotics 11.2 (2022), p. 44.
[207] A. A. Shabana and A. E. Eldeeb. “Motion and shape control of soft robots andmaterials”. In: Nonlinear dynamics 104 (2021), pp. 165–189.
[208] S. Chen, Y. Cao, M. Sarparast, H. Yuan, L. Dong, X. Tan, and C. Cao. “Soft crawlingrobots: design, actuation, and locomotion”. In: Advanced Materials Technologies5.2 (2020), p. 1900837.
[209] M. Calisti, G. Picardi, and C. Laschi. “Fundamentals of soft robot locomotion”.In: Journal of The Royal Society Interface 14.130 (2017), p. 20170101.

人工提交

Su YY. Deformation-based Multimodal Perception for Soft Pneumatic Robots[D]. Hong Kong. The University of Hong Kong,2024.