Obstacle avoidance of redundant manipulators using a dual neural network

Abstract

One important issue in motion planning and kinematic control of redundant manipulators is the real-time obstacle avoidance. Following the previous researches, a new problem formulation has been proposed in the sense that the collision avoidance scheme is described by dynamically-updated inequality constraints, and that physical constraints such as joint limits are also incorporated in the formulation. For real-time computation, the dual neural network is applied for the online solution of obstacle-avoidance inverse-kinematic control problem, and then simulated based on the PA10 robot manipulator in the presence of obstacles.