Research on Warehouse Path Planning for Logistics Robots Based on Improved Whale Optimization Algorithm

How to efficiently and reasonably carry out path planning has always been a hot spot of research in the field of warehouse logistics robots. We propose a logistics robot warehouse path planning strategy based on the Improved Whale Optimisation Algorithm (IWOA). The whale optimisation algorithm’s convergence speed is slow, making it easy to fall into the limitations of the local optimum. First, we use Logistic Chaos-based population initialisation to ensure the diversity of algorithmic individuals. Second, we use adaptive weights in the whale bubble behaviour, which can effectively avoid the algorithm from falling into the local optimum. Finally, we use the Bresenham straight line method in the warehouse path movement. Simulation experiments, the algorithm of this paper and Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), Whale Optimisation Algorithm (WOA) in different benchmark functions for comparison, the results show that the algorithm has a better effect, in the 20 * 20 unit area in the simulation of four different warehouse scenarios, the results show that the algorithm of this paper has a clear advantage over the ACO, PSO advantage, and compared to the WOA algorithm path length reduced by 1.94%, which shows that the The algorithm can make the robot adapt to the more congested warehouse scenarios.