## 6 Future Prospects  
Although our proposed hybrid planning strategy achieves strong performance, several directions can further enhance the find of AGV path in complex manufacturing environments, including the following:  
Pilot Operation in Real Manufacturing SCenarios: The validation of the method proposed in this paper has thus far relied entirely on simulation experiments, which inherently cannot cover every scenario encountered in real manufacturing environments. Its applicability on actual production or logistics sites remains unverified. Therefore, in future work, we will build more realistic testbeds and seek collaboration with industry partners to conduct pilot runs in environments such as dark warehouses(fully automated unit facilities warehouse).  
Improve System Robustness: Our distributed planning strategy depends on communications with both the central controller and the neighboring AGVs. If an AGV goes offline due to a fault, its transport tasks will be disrupted. To address this, it is necessary to train a purely local data-based fallback planner as an emergency contingency. In addition, wehave not yet introduced an explicit deadlock avoidance mechanism. AGVs learn implicit coordination behaviors via communication-based training. But no training process can cover all possible deadlock scenarios. Hence, we will monitor AGV task execution in real time and incorporate an explicit deadlock-avoidance policy in future work.  
Deeper Integration of Heuristics and Reinforcement Learning: The heuristic function in A* shares conceptual similarities with the reinforcement learning reward function, since both estimate future path costs. Future work could explore integrating heuristics into reinforcement learning rewards, where states closer to the goal receive lower penalties, further mitigating the sparse reward issue and improving training efficiency.  
Expanding AGV Decision-Making Strategies: In our current model, AGVs follow two main decision-making strategies--obstacle avoidance (micro-level autonomy) and following pre-planned paths (macro-level control). Future work could introduce multiple obstacle avoidance strategies, allowing AGVs to adapt their navigation behavior based on local congestion levels, obstacle types, and operational zones. Additionally, AGVs could dynamically switch between centralized and decentralized solvers, optimizing pathfinding based on the real-time complexity of their environment.  
Task Scheduling Optimization: While this paper focuses on pathfinding, task scheduling plays a crucial role in optimizing AGV efficiency in lifelong scenarios. Future research could explore intelligent task allocation methods for minimizing task switching delays, optimizing AGV workload balancing, and reducing idle time in dynamic task assignment scenarios.  
By incorporating these advancements, AGV pathfinding frameworks can become even more efficient, scalable, and adaptable, ultimately improving the overall performance of smart logistics and industrial automation systems.  
Conceptualization, L.H. and J.Z.; methodology, J.L. and J.Z.; software, J.Z. and J.L.; validation, J.Z. and J.L.; writing--original draft preparation, J.L.; writing--review and editing, L.H. and J.Z.; funding acquisition and resources, L.H. and J.Z.; supervision, L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Pioneer\" R&D Program of Zhejiang (Grant No. 2025C01088).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.