Path Planning of Robot Based on Improved Multi-Strategy Fusion Whale Algorithm

In logistics and manufacturing, smart technologies are increasingly used, and warehouse logistics robots (WLR) have thus become key automation tools. Nonetheless, the path planning of mobile robots in complex environments still faces the challenges of excessively long paths and high energy consumption. To this end, this study proposes an innovative optimization algorithm, IWOA-WLR, which aims to optimize path planning and improve the shortest route and smoothness of paths. The algorithm is based on the Whale Algorithm with Multiple Strategies Fusion (IWOA), which significantly improves the obstacle avoidance ability and path optimization of mobile robots in global path planning. First, improved Tent chaotic mapping and differential dynamic weights are used to enhance the algorithm’s optimization-seeking ability and improve the diversity of the population. In the late stage of the optimization search, the positive cosine inertia threshold and the golden sine are used to perform adaptive position updating during the search strategy to enhance the global optimal search capability. Secondly, the fitness function of the path planning problem is designed, and the path length is taken as the objective function, the path smoothness as the evaluation index, and the multi-objective optimization is realized through the hierarchical adjustment strategy and is applied to the global path planning of WLR. Finally, simulation experiments on raster maps with grid sizes of 15 × 15 and 20 × 20 compare the IWOA algorithm with the WOA, GWO, MAACO, RRT, and A* algorithms. On the 15 × 15 maps, the IWOA algorithm reduces path lengths by 3.61%, 5.90%, 1.27%, 15.79%, and 5.26%, respectively. On the 20 × 20 maps, the reductions are 4.56%, 5.83%, 3.95%, 19.57%, and 1.59%, respectively. These results indicate that the improved algorithm efficiently and reliably finds the global optimal path, significantly reduces path length, and enhances the smoothness and stability of the path’s inflection points.