Distance-directed Target Searching for a Deep Visual Servo SMA Driven Soft Robot Using Reinforcement Learning

• 作者: Han Pan Henry Leung Lingfeng Qiao Wuji Liu Wujun Chen Zhongliang Jing
• 来源期刊: 仿生工程学报（英文版）
• 年，卷（期）: 2020,（6）
• 页码范围: 1126-1138
• 页数: 13页
• 语种: 英文
• DOI: 10.1007/s42235-020-0102-8

摘要

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-learning 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 leaming process and effectiveness of the proposed algorithm.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	Wuji Liu		1	0	0.0	0.0
2	Zhongliang Jing		1	0	0.0	0.0
3	Han Pan		1	0	0.0	0.0
4	Lingfeng Qiao		1	0	0.0	0.0
5	Henry Leung		1	0	0.0	0.0
6	Wujun Chen		1	0	0.0	0.0