Full Coverage Path Planning for Sweeping Robot Based on Memory Simulated Annealing and Ant Colony Algorithm

A traversal strategy combining a memory-based simulated annealing algorithm with Ant Colony Optimization (ACO) is proposed to address the high redundancy and weak universality issues in full coverage path planning for indoor robotic operations. Initially, the target area is subdivided into multiple subregions. Subsequently, the optimal traversal sequence for these partitions is determined using the memory-based simulated annealing algorithm. Then, the ACO algorithm effectively connects these partitions together, and an appropriate comb traversal algorithm is employed to traverse the interior of the subregions. Simulation results demonstrate significant advantages of the proposed full coverage traversal algorithm, achieving a coverage rate of 100% with a traversal redundancy rate of 3.25%. Furthermore, the improved Simulated Annealing (SA) algorithm, augmented with a memory unit, mitigates the risk of losing the optimal solution encountered thus far during the probability acceptance stage, thereby facilitating escape from local optima and enhancing solution quality. The subregion traversal sequence obtained through the enhanced SA algorithm contributes to enhancing the efficiency of robotic operations in full coverage path planning.