A Data-Driven Obstacle Avoidance Scheme for Redundant Robots With Unknown Structures

Abstract

Redundant robots may undergo structural changes due to factors such as modifications, which pose challenges to their precise control and obstacle avoidance. To resolve this issue, this article proposes a data-driven obstacle avoidance (DDOA) scheme for redundant robots with unknown structures, which integrates obstacle avoidance control and structure learning. To ensure collision-free operations, an obstacle avoidance method for redundant robots is devised to maintain a safe distance from obstacles. Simultaneously, a data-driven learning equation is developed to estimate two Jacobian matrices of robots for obstacle avoidance and motion planning. A recurrent neural network (RNN) is then established to find the optimal solution to the DDOA scheme with theoretical analyses. Furthermore, we demonstrate the learning and control capabilities of the proposed RNN by providing illustrative simulations and experiments on a Franka Emika Panda robot. The results exhibit significant collision avoidance and learning performance of the proposed method with tiny errors.