Research on Distributed Multi-Robot Path Planning Based on Optimized Ant Colony Optimization Algorithm

With the rapid advancement of robotics, multi-robot systems (MRS) have found extensive applications across various domains, including logistics, environmental monitoring, and post-disaster rescue operations. Effective path planning is crucial for facilitating the cooperative operation of multiple robots. This paper proposes a collaborative path planning method based on Optimized Ant Colony Optimization (OACO) and systematically compares it with traditional Ant Colony Optimization (ACO) and Model Predictive Control (MPC) in the context of single-robot path planning, as well as in multi-robot collision avoidance scenarios. The comparison with ACO reveals that OACO reduces the path length by approximately 10%, decreases the running time by 35%, and lowers the number of iterations by 27%. Additionally, the length variance indicates the stability of OACO in complex environments. When compared to MPC, OACO achieves a path length reduction of about 15% and demonstrates greater suitability for addressing global path planning challenges characterized by high complexity. These findings suggest that OACO exhibits high efficiency and accuracy in multi-robot collaborative path planning, making it well-suited for applications in complex dynamic environments.