Path Planning for Intelligent Warehouse Robots Based on a Jump Point Search-Enhanced Ant Colony Optimization Algorithm

In the context of warehouse management systems, navigation constitutes a critical area of research for enhancing operational efficiency. This paper introduces a novel hybrid algorithm designated as jump point search-enhanced ant colony optimization (JPS-EACO). Initially, the jump point search (JPS) algorithm generates a preliminary path rapidly. Subsequently, pheromone values are distributed in the vicinity of this path, establishing a non-uniform initial pheromone distribution across the entire environmental grid. To bolster the algorithm’s global search capacity, the heuristic function of the standard ant colony optimization (ACO) is refined. Furthermore, an adaptive pheromone evaporation strategy is integrated to regulate the pheromone update process throughout the iterative procedure. Additionally, the optimal route generated by the algorithm undergoes refinement. This involves the elimination of superfluous nodes and the smoothing of corners through the application of Bézier curves, which enhances the path’s smoothness and practical feasibility. The performance of the proposed JPS-EACO method was evaluated through simulations conducted on grid environments of dimensions 20 × 20 and 30 × 30. For the 20 × 20 grid, the algorithm demonstrated rapid convergence, requiring an average of 1.9 iterations. It achieved a reduction in route length of 5.21% compared to several reference algorithms. In the 30 × 30 scenario, the mean number of iterations was 8.2, and the resultant path length was shortened by a minimum of 3.64%. Moreover, in 40 × 40 and 50 × 50 grid environments, the algorithm also demonstrates stable and superior performance. These outcomes indicate that JPS-EACO offers faster convergence, shorter paths, and superior path smoothness. Finally, validation through real-world experiments confirmed the method’s effectiveness and its practical applicability.