Performing complex tasks by soft robots in constrained environment remains an enormous challenge owing to the limitations of flexible mechanisms and control methods.In this paper,a novel biomimetic soft robot driven b...Performing complex tasks by soft robots in constrained environment remains an enormous challenge owing to the limitations of flexible mechanisms and control methods.In this paper,a novel biomimetic soft robot driven by Shape Memory Alloy(SMA)with light weight and multi-motion abilities is introduced.We adapt deep learning to perceive irregular targets in an unstructured environment.Aiming at the target searching task,an intelligent visual servo control algorithm based on Q-leaming is proposed to generate distance-directed end effector locomotion.In particular,a threshold reward system for the target searching task is proposed to enable a certain degree of tolerance for pointing errors.In addition,the angular velocity and working space of the end effector with load and without load based on the established coupling kinematic model are presented.Our framework enables the trained soft robot to take actions and perform target searching.Realistic experiments under different conditions demonstrate the convergence of the learning process and effectiveness of the proposed algorithm.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(Grant no.61673262)in part by the key project of Science and Technology Commission of Shanghai Municipality(Grant no.16JC1401100).
文摘Performing complex tasks by soft robots in constrained environment remains an enormous challenge owing to the limitations of flexible mechanisms and control methods.In this paper,a novel biomimetic soft robot driven by Shape Memory Alloy(SMA)with light weight and multi-motion abilities is introduced.We adapt deep learning to perceive irregular targets in an unstructured environment.Aiming at the target searching task,an intelligent visual servo control algorithm based on Q-leaming is proposed to generate distance-directed end effector locomotion.In particular,a threshold reward system for the target searching task is proposed to enable a certain degree of tolerance for pointing errors.In addition,the angular velocity and working space of the end effector with load and without load based on the established coupling kinematic model are presented.Our framework enables the trained soft robot to take actions and perform target searching.Realistic experiments under different conditions demonstrate the convergence of the learning process and effectiveness of the proposed algorithm.
