Obstacle Avoidance for Kinematically Redundant Manipulators Based on an Improved Problem Formulation and the Simplified Dual Neural Network

With the wide deployment of kinematically redundant manipulators in complex working environments, obstacle avoidance emerges as an important issue to be addressed in robot motion planning. In this paper, the inverse kinematic control of redundant manipulators with obstacle avoidance task is formulated into a (convex) quadratic programming (QP) problem with both equality and inequality constraints. Compared with our previous formulation, the new scheme is more favorable in the sense that it can yield better solutions to the control problem. To solve this time-varying QP problem in real time, a newly emergent recurrent neural network, simplified dual neural network, is adopted, which has lower structural complexity compared with existing neural networks for solving QP problems. The effectiveness of the proposed approach is demonstrated by using simulations on the Mitsubishi PA10-7C manipulator.