Ren, Q., Zhu, W., Cao, J., & Liang, W. (2023). Neuro-Inspired Motion Control of a Soft Myriapod Robot. IEEE Transactions on Cognitive and Developmental Systems, 1–1. https://doi.org/10.1109/tcds.2023.3284399
Abstract:
Centipedes exploit soft structures to move efficiently in complex environments. These abilities have promoted scientists to develop centipede-like robots with soft robot technologies. In this paper, a centipede-like robot constructed by multiple body segments connected in series is designed. Each body segment features a pair of antagonistic artificial muscles and four electro-adhesive feet, which plays the role of the basic motion unit of the centipede-like robot and can be regarded as a soft myriapod robot. To accomplish the desired movement tasks for the soft myriapod robot, this paper proposes a neuro-inspired hierarchical motion control scheme according to the functions of the centipede brain. In the proposed control scheme, a mushroom-body-inspired controller with an error-based learning mechanism is adopted for the soft actuator motion control, and a central-complex-inspired deep reinforcement learning algorithm is designed for the robot motion selection. At last, the effectiveness of the proposed motion control scheme is verified via both numerical studies and experiments.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Robotic HTCO Seed Fund
Grant Reference no. : C211518005