Obstacle avoidance and model‐free tracking control for home automation using bio‐inspired approach

Abstract

AbstractThis article proposes a control algorithm for obstacle avoidance and trajectory tracking for a redundant‐manipulator in smart‐homes. The redundancy provides dexterity and flexibility for the applications like picking, dropping, and transporting objects, tracking predefined paths while avoiding obstacles. The obstacle avoidance is one of the critical problems that need to be addressed. Our proposed algorithm, zeroing neural network with beetle antennae search (ZNNBAS), unifies these two problems into a single constrained optimization problem, which includes a penalty function to reward the optimizer on avoiding obstacles while tracking the reference trajectory. The penalty function is based on the principle of maximizing the minimum distance between the joints of the manipulator and the obstacles. Gilbert Johnson Keerthi (GJK) algorithm is used to calculate the distance between the manipulator and the obstacle as it uses the 3D geometry of the object and manipulator. To test the working of ZNNBAS, we simulated the model of the KUKA LBR robotic manipulator. We used two reference trajectories, that is, hypotrochoid and a character “M,” with an arbitrarily shaped obstacle. The simulation results show that ZNNBAS was able to trace the reference path while successfully avoiding the obstacle accurately.