Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network

Wang, Liangliang1; Lam, James1; Chen, Xiaojiao1; Li, Jing1; Zhang, Runzhi1; Su, Yinyin1,2; Wang, Zheng2

2023-03-01

10.1089/soro.2021.0056

SOFT ROBOTICS   影响因子和分区

2169-5172

2169-5180

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.

proprioception soft pneumatic chamber receptor pressure information kinematic model receptor failure

SCI ; EI

英语

通讯

Science, Technology and Innovation Commission of Shenzhen Municipality[ZDSYS20200811143601004] ; NSFC[51975268]

Robotics

Robotics

WOS:000961052800001

MARY ANN LIEBERT, INC

20232714343012

Kinematics ; Pneumatic actuators ; Pneumatics ; Recurrent neural networks ; Robots ; Sensory perception ; Signal encoding

Ergonomics and Human Factors Engineering:461.4 ; Pneumatics:632.3 ; Information Theory and Signal Processing:716.1 ; Robotics:731.5 ; Control Equipment:732.1 ; Mechanics:931.1

Web of Science

1.Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China
2.Southern Univ Sci & Technol, Dept Mech & Energy Engn, 605 Innovat Pk 7,1088 Xueyuan Ave, Shenzhen 518055, Peoples R China

Wang, Liangliang,Lam, James,Chen, Xiaojiao,et al. Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network[J]. SOFT ROBOTICS,2023,10(4):825-837.

Wang, Liangliang.,Lam, James.,Chen, Xiaojiao.,Li, Jing.,Zhang, Runzhi.,...&Wang, Zheng.(2023).Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network.SOFT ROBOTICS,10(4),825-837.

Wang, Liangliang,et al."Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network".SOFT ROBOTICS 10.4(2023):825-837.