# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 5 Conclusions  
In a complex dynamic warehouse environment, the multi-mobile robot system faces a complex environment, and the GWO algorithm has difficulty quickly and accurately finding an optimal path to the target point, resulting in low efficiency. An improved grey wolf optimization algorithm (LAPGWO) combining the logistic chaotic mapping and the artificial potential field method (APF) is proposed. Based on the logistic chaotic mapping, a uniform chaotic sequence is generated to increase the diversity of the population and improve the search efficiency. At the same time, chaotic disturbances are introduced when updating the positions of grey wolves to avoid premature convergence. Based on the APF, gravitational and repulsive force fields are constructed to promote the movement of grey wolves toward the target point and keep grey wolves away from obstacles, further accelerating the search speed.  
Given the dynamic characteristics of the warehouse environment, optimization and improvement are carried out based on the artificial potential field method. A repulsive force field between robots is constructed to avoid collisions between robots. A repulsive force correction term function is built to solve the problem of unreachable targets. The generated path is smoothed using the Cantmull--Rom spline curve to improve path continuity and traceability. A path restoration function is constructed to enable the robot to return to the global path smoothly after obstacle avoidance and reduce path redundancy. The advancement and practical applicability of the method in this paper are further verified through simulation and physical comparison experiments. In contrast, the experiments in this paper are mainly conducted in simulation and relatively ideal environments. When in an actual complex environment, the environment is imperfect, and issues such as sensor noise and communication delays must be considered. When facing other environments, the selection of various parameters still requires a large number of repeated tests to obtain the optimal range. In the follow-up, this paper will conduct the adaptive optimization of the parameters to meet the application requirements of different scenarios.  
In addition, the experiments in this paper are mainly carried out in simulations and relatively ideal environments. In an actual complex environment, the environment is not in an ideal state, and many factors must be considered. In the future, we will further optimize the method, seek solutions to possible problems in practical applications, and apply it to the actual workspace.  
Figure 20: The formation experiment process of the physical experimental platform.  
**Author Contributions:** Conceptualization, D.L. and W.X.; methodology, D.L. and W.X.; software, A.N.; validation, D.L. and J.W.; formal analysis, D.L. and S.L.; investigation, D.L.; resources, D.L. and A.N.; data curation, D.L.; writing--original draft preparation, D.L.; writing--review and editing, D.L.; visualization, D.L.; supervision, D.L. and W.X; project administration, W.X. and D.L.; funding acquisition, W.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Key Research and Development Program of Hubei Province under grant no. 2023 BEB031.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article material. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.