Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

Abstract

This paper presents a metaheuristic-based control framework, called Beetle Antennae Olfactory Recurrent Neural Network (BAORNN), for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle avoidance into a single constrained optimization problem by introducing a penalty term into the objective function, which actively rewards the optimizer for avoiding the obstacles. One of the significant features of the proposed framework is the way that the penalty term is formulated following a straightforward principle: maximize the minimum distance between manipulator and obstacle. The distance calculations are based on GJK (Gilbert-Johnson-Keerthi) algorithm, which calculates the distance between manipulator and obstacle by directly using their 3D-geometries. Which also implies that our algorithm works for arbitrarily shaped manipulator and obstacle. Theoretical treatment proves the stability and convergence, and simulations results using LBR IIWA 7-DOF manipulator are presented to analyze the performance of the proposed framework.