Hybridization of Kidney-Inspired and Sine–Cosine Algorithm for Multi-robot Path Planning

Abstract

A hybridization of kidney-inspired and sine–cosine algorithm has been proposed for a path planning of multiple mobile robots in the environment where obstacles are either static or moveable. In this novel approach, each robot computes its collision-free optimal path from their corresponding start position to goal position through hybridization of kidney-inspired algorithm (KA) and sine–cosine algorithm (SCA). The proposed KA–SCA employs the selection of subsequent optimal position for each robot from their current position by escaping the collision with dynamic obstacles and teammates. In the present work, SCA is used to accelerate the convergence rate of KA, to preserve a good equilibrium between the intensification and diversification, and to compute an optimal path for each robot by minimizing the path distance, path deviation, number of rotation for each robot, and running time required to reach their destination. Finally, the effectiveness and robustness of the proposed algorithm have been verified with the result of KA and SCA in the same environment. The result obtained from the real platform and simulation environment reveals that the proposed KA–SCA outperforms KA and SCA.