# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 2 Related Work
This allows different grey wolf populations to complete different tasks during the training process, thus accelerating the algorithm's convergence. [PERSON] [27] integrated the Archimedes optimization algorithm with the grey wolf optimizer (GWO) to improve convergence and generate an optimal route in a complex environment. However, it is prone to becoming trapped in local optima and has an extended search time. [PERSON] [28] proposed an optimized algorithm, the chaotic white shark optimizer (CWSO). Integrating chaotic maps into the optimization process is particularly advantageous, as it broadens the search capability, accelerates convergence, and reduces the likelihood of falling into local minima. [PERSON] [29] proposed the chaotic Kepler optimization algorithm (CKOA). Based on the dynamic diversification strategy of chaotic maps, it is characterized by its ability to better avoid local minima and reach the global optimal solution. It integrates chaotic maps to solve complex engineering problems. [PERSON] [30] proposed an innovative DRL-MPC-GNNs model, which integrates deep reinforcement learning, model predictive control (MPC), and graph neural networks (GNNs). This improves path planning accuracy, task allocation efficiency, and robot collaborative performance. However, it sacrifices computational time when dealing with complex environments, resulting in lower efficiency. In response to the deficiencies mentioned above, this paper proposes a solution strategy based on distributed online path planning architecture. By combining the logistic chaotic map with the grey wolf algorithm and introducing the potential field of the artificial potential field method, it aims to find the optimal global path. After the worldwide path search is completed, the artificial potential field method achieves local dynamic obstacle avoidance, realizing the path planning of multiple mobile robots in complex environments. This approach can ensure computational efficiency and, at the same time, guarantee the task completion rate and conflict rate in a multi-mobile robot system. The LAPGWO (the algorithm proposed in this paper), for the first time, deeply integrates the global exploration ability of the chaotic map with the real-time obstacle avoidance characteristics of the APF (artificial potential field), solving the problems of path jitter and the local convergence of traditional GWO variants in scenarios with dynamic obstacles. The specific contributions are as follows:  
1. In the warehouse environment, due to the large number of storage shelves, the initial population of the GWO algorithm fails to distribute evenly, causing the algorithm to miss the global optimal solution in the early stage. To solve this problem, a logistic chaotic mapping method is introduced to enhance the diversity of the population. 2. In the warehouse environment, the GWO algorithm suffers from insufficient global search capabilities, resulting in long search times and low efficiency. To tackle this, we propose introducing factors from the APF method. The attractive potential field accelerates the algorithm's approach toward the target point, improving the global search capabilities of the GWO algorithm and the system's efficiency. 3. In the warehouse environment, the abundance of shelf-like obstacles leads to excessive repulsive forces, making it impossible for robots to reach their targets. To address this, a dynamic repulsive potential field function is introduced to solve the problem of unreachable targets. During the movement of robots, collisions with other robots are likely to occur. Thus, each robot's independent potential field is constructed to avoid collisions. After obstacle avoidance, an attraction function is established based on human shortcut-taking behavior principles to enable robots to return to the preset trajectory smoothly.