Multi-robot Path Planning in Dynamic Environments Using the Coati Optimization Algorithm

Abstract

Multi-robot path planning in complex dynamic environments is a challenging optimization problem due to the simultaneous requirements of path efficiency, obstacle avoidance, and inter-robot collision prevention. To address these challenges, this study applies the Coati Optimization Algorithm (COA), a bio-inspired swarm intelligence optimization method, to multi-robot path planning. The trajectories of multiple robots are represented by a set of continuous control waypoints, and a comprehensive objective function is formulated by jointly considering path length, static obstacle avoidance, and inter-robot collision penalties. By exploiting the global exploration and local exploitation capabilities of COA, feasible and smooth collision-free trajectories can be generated in environments containing both static and dynamic obstacles. Simulation experiments were conducted in MATLAB R2024a with 12 mobile robots, 9 static obstacles, and 5 dynamic obstacles, and the performance of COA was compared with that of the A* algorithm. The results show that COA produces smoother paths with fewer unnecessary turns and reduces the total travel distance from 670.9 to 617.8355, corresponding to an improvement of approximately 7.9%. These results demonstrate that COA provides an effective approach for coordinated multi-robot path planning in complex dynamic environments and has potential for further application to practical multi-AGV and mobile robot systems.