A hybrid path planning algorithm combining A and improved ant colony optimization with dynamic window approach for enhancing energy efficiency in warehouse environments.

This research presents a novel hybrid path planning algorithm combining A*, ant colony optimization (ACO), and the dynamic window approach (DWA) to enhance energy efficiency in warehouse environments. The proposed algorithm leverages the heuristic capabilities of A*, the optimization strengths of ACO, and the dynamic adaptability of DWA. Experimental results demonstrate that the IACO+A*+DWA approach consistently outperforms other hybrid methods across various metrics. In complex warehouse scenarios, the IACO+A*+DWA algorithm achieved an average energy consumption of 89.8 J, which is 13.3% lower than ACO+A*+DWA, 6.6% lower than GA+A*+DWA, and 25.8% lower than PSO+A*+DWA. The algorithm produced a path length of 95.94 m with 43 turns, compared to 97.36 m with 46 turns for ACO+A*+DWA, 104.43 m with 50 turns for GA+A*+DWA, and 97.84 m with 56 turns for PSO+A*+DWA. Time to goal was 197.93 s, 1.5% faster than GA+A*+DWA. Statistical analysis using ANOVA confirmed the significant differences between the algorithms in terms of energy consumption, path length, number of turns, and time taken, demonstrating the superior performance of IACO+A*+DWA. These results indicate that the IACO+A*+DWA algorithm minimizes energy consumption and produces shorter and more efficient paths, making it highly suitable for real-time applications in dynamic and complex warehouse environments. Future work will focus on further optimizing the algorithm and integrating machine learning techniques for enhanced adaptability and performance.