Foam-Embedded Soft Robotic Joint With Inverse Kinematic Modeling by Iterative Self-Improving Learning

Soft robotic arms have gained significant attention owing to their flexibility and adaptability. Nonetheless, the instability due to their high-elasticity structure further leads to the difficulty of precise kinematic modeling and control. This letter introduces a novel solution employing foam-embed...

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Bibliographic Details
Published inIEEE robotics and automation letters Vol. 9; no. 2; pp. 1756 - 1763
Main Authors Huang, Anlun, Cao, Yongxi, Guo, Jiajie, Fang, Zhonggui, Su, Yinyin, Liu, Sicong, Yi, Juan, Wang, Hongqiang, Dai, Jian S., Wang, Zheng
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Actuators
Arms
Bellows
Design optimization
Inverse kinematics
Iterative methods
Kinematics
Learning
Manipulators
Mechanical properties
Modelling
Motion stability
oscillation reduction
Oscillators
Robot arms
Robotics
self-improving learning
Soft robotic joint
Soft robotics
Trajectories
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Summary:Soft robotic arms have gained significant attention owing to their flexibility and adaptability. Nonetheless, the instability due to their high-elasticity structure further leads to the difficulty of precise kinematic modeling and control. This letter introduces a novel solution employing foam-embedded joint design (Fe-Joint), effectively mitigating oscillations and enhancing motion stability. This innovation is integrated into the new continuum soft robotic arm (Fe-Arm). Through iterative design optimization, the Fe-Arm attains superior mechanical performance and control capabilities, enabling a settling state in 0.4 seconds post external force. Enabled by the quasi-static behavior of Fe-Arm, we propose a long short-term memory network (LSTM) based iterative self-improving learning strategy (ISL) for end-to-end inverse kinematics modeling, tailored to Fe-Arm's mechanical traits, enhancing modeling performance with limited data. Investigating key control parameters, we achieve target trajectory modeling errors within 9% of the workspace radius. The generalization potential of the ISL method is demonstrated using the pentagonal trajectory and on a different Fe-Arm configuration.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3349831