Deep RNN Based Obstacle Avoidance Control for Redundant Manipulators
Deep RNN Based Obstacle Avoidance Control for Redundant Manipulators
- New search for: Zhou, Xuefeng
- New search for: Xu, Zhihao
- New search for: Li, Shuai
- New search for: Wu, Hongmin
- New search for: Cheng, Taobo
- New search for: Lv, Xiaojing
- New search for: Zhou, Xuefeng
- New search for: Xu, Zhihao
- New search for: Li, Shuai
- New search for: Wu, Hongmin
- New search for: Cheng, Taobo
- New search for: Lv, Xiaojing
In this chapter, we consider the obstacle avoidance problem of redundant robot manipulators with physical constraints compliance, where static and dynamic obstacles are investigated. Both the robot and obstacles are abstracted as two critical point sets, respectively, relying on the general class-K functions, the obstacle avoidance problem is formulated into an inequality in speed level. The minimal-velocity-norm (MVN) is regarded as the cost function, converting the kinematic control problem of redundant manipulators considering obstacle avoidance into a constraint-quadratic-programming problem, in which the joint angles and joint velocity constraints are built in velocity level in form of inequality. To solve it, a novel deep recurrent neural network based controller is proposed. Theoretical analyses and the corresponding simulative experiments are given successively, showing that the proposed neural controller does not only avoid collision with obstacles, but also track the desired trajectory correctly.
Document information
Deep RNN Based Obstacle Avoidance Control for Redundant Manipulators
AI based Robot Safe Learning and Control ; Chapter : 4 ; 63-81
2020-06-03
19 pages
Article/Chapter (Book)
Electronic Resource
English
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