Bipedal Walking of Underwater Soft Robot Based on Data-Driven Model Inspired by Octopus - Wu, Qiuxuan; Wu, Yan; Yang, Xiaochen; Zhang, Botao; Wang, Jian; Chepinskiy, Sergey A; Zhilenkov, Anton A (Frontiers in Robotics and AI, 2022)
The soft organisms in nature have always been a source of inspiration for the design of soft arms and this paper draws inspiration from the octopus's tentacle, aiming at a soft robot for moving flexibly in three-dimensional space. In the paper, combined with the characteristics of an octopus’s tentacle, a cable-driven soft arm is designed and fabricated, which can motion flexibly in three-dimensional space. Based on the TensorFlow framework, a data-driven model is established, and the data-driven model is trained using deep reinforcement learning strategy to realize posture control of a single soft arm. Finally, two trained soft arms are assembled into an octopus-inspired biped walking robot, which can go forward and turn around. Experimental analysis shows that the robot can achieve an average speed of 7.78 cm/s, and the maximum instantaneous speed can reach 8.5 cm/s.