Path Planning for Mobile Robots Based on Multi-strategy Enhanced Ant Colony Optimization Algorithm

Path planning is a fundamental challenge of mobile robots, involving the determination of optimal paths in complex environments. Although Ant Colony Optimization (ACO) has shown promise for this task, it often suffers from slow convergence and susceptibility to local optima. To address these limitations, a Multi-Strategy Enhanced ACO (MsEACO) algorithm for mobile robot path planning is proposed in this paper. First, a non-uniform initial pheromone setting strategy is introduced to bias initial ant exploration towards promising regions of the search space. Second, improved state transition probability functions are developed, incorporating turn angle and distance information to enhance inspiration and facilitate targeted exploration. Third, a comprehensive pheromone update strategy is designed to accelerate convergence and escape local optima. Parameter optimizations and individual contributions of these three enhanced strategies were performed separately across three benchmark maps. Comparative simulation experiments conducted on six diverse maps demonstrate that MsEACO significantly outperforms the traditional ACO, two recent ACO variants, and five non-ACO based metaheuristic algorithms. Specifically, MsEACO achieves average reduction of 32.88% in path length, 68.40% in turn times, and 92.97% in iterations compared to traditional ACO. These results highlight the potential of MsEACO for efficient and reliable mobile robot path planning in diverse scenarios.