Abstract

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.

Highlights

  • With development of intelligent manufacturing and automation, the research on robot manipulators is obtaining increasing attention from a large number of scholars, numerous fruits have been reported on painting, welding and assembly [1, 2] and so on

  • Stem from the consideration of human-machine collaboration, robots are no longer arranged in a separate area [6–8], which makes the obstacle avoidance for robots become an important part of kinematic control of the robot manipulators

  • Motivated by the above observations, in this chapter, a RNN-based obstacle avoidance strategy was proposed for redundant robot manipulators. Both the robot and obstacles are abstracted as two critical point sets, respectively, relying on the class-K functions, the obstacle avoidance problem is formulated into an inequality in speed level

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Summary

Introduction

With development of intelligent manufacturing and automation, the research on robot manipulators is obtaining increasing attention from a large number of scholars, numerous fruits have been reported on painting, welding and assembly [1, 2] and so on. More and more scholars cast light on redundant robots which show better flexibility, responsiveness [4, 5]. Stem from the consideration of human-machine collaboration, robots are no longer arranged in a separate area [6–8], which makes the obstacle avoidance for robots become an important part of kinematic control of the robot manipulators. There has reported many obstacle avoidance methods applicable to robot manipulators. A modified RRT based method, namely Smoothly RRT, was proposed in [9]. This paper established a maximum curvature constraint to obtain a smooth curve when avoiding obstacles.

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