Multi-Robot Navigation Uses the Greedy Heuristic Algorithm Based on the ACO Algorithm

Abstract

Robot swarm navigation faces challenges in coordi-nation in dynamic environments with limited information, since each robot only has local access, making optimal paths difficult to achieve. Increasing the number of robots also increases the complexity of communication and decision-making, while energy efficiency remains a priority. The Ant Colony Optimization (ACO) algorithm mimics the behavior of ants through pheromone trails, allowing robots to leave virtual trails that guide other robots in forming optimal paths, allowing them to adapt to changing environments. In this paper, we propose an optimal multi-robot navigation system with ACO based on Greedy Heuristics to improve the efficiency of robots in dealing with static and dynamic obstacles, avoiding collisions, and preventing deadlocks. The test results obtained the shortest distance of 31.2132034 meters with alpha parameters of 3 points and beta of 4 points as the optimum parameter configuration according to the method used.