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...
Saved in:
Published in | IEEE robotics and automation letters Vol. 9; no. 2; pp. 1756 - 1763 |
---|---|
Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
Cover
Loading…
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 |