# Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems
## 5 Conclusions  
This study presents a bio-inspired approach for the autonomous generation of movement constraints in multi-AMR systems, demonstrating how an optimization mechanism based on four basic principles--movement rewards, collision penalties, delay feedback, and evaporation--can effectively improve system performance. It was shown that spatial constraints can proactively reduce the number of conflict situations without temporal coordination. The efficiency of the proposed method was validated through both event-based and physics-based simulations. The solutions obtained with our method consistently outperformed both unconstrained navigation and expert-designed solutions. Significant improvements in system performance were observed in several test scenarios, including standard MAPF benchmarks and real industrial environments. Compared to other existing approaches, our method offers several advantages. First, the method integrates complete system characteristics, including layout, fleet size, task distribution, and dispatching algorithms, into the optimization process and tailors the  
Figure 12: Performance comparison across scenarios. Bars show tasks completed with and without recovery actions; whiskers indicate the standard deviation of total task completions. solution to the specific intralogistics problem. The existing approaches usually only take into account the layout. Second, the method can successfully handle complex spatial constraints (e.g., narrow corridors), which reduces the need for recovery actions while ensuring high throughput. Third, solution generation is very fast and typically requires only a few minutes, even for complex layouts. If the system parameters change (increased demand, different material flow, fleet size, etc. ), the movement constraints can be adapted quickly and without manual redesign. The adaptability and low computational complexity of the proposed approach provide flexibility that traditional design approaches lack, making it particularly practical for dynamic real-world applications. The successful integration with the ROS2 Nav2 stack through a custom global planner plugin also demonstrates the practical applicability of the approach in standard robotics frameworks. However, there are some limitations of the proposed method that should be considered. We have shown that conflict situations with other vehicles can be proactively reduced through spatial constraints, but the performance advantages diminish in extremely dense traffic situations, where temporal coordination becomes essential. For real-world deployment, our approach could be integrated with state-of-the-art scheduling algorithms to jointly address spatial and temporal coordination. This can lead to additional improvements in system efficiency. Another limitation of the proposed method is that it assumes static obstacles, while the resolution of conflicts with dynamic obstacles is left to the local planner. It also does not incorporate real-time adaptability mechanisms to adjust movement constraints dynamically based on observed traffic fluctuations. To enhance real-world applicability, several future directions should be explored. First, the optimization algorithm should be extended to incorporate dynamic parameter adjustment, enabling more effective adaptation to varying traffic densities and patterns. Second, integrating reinforcement learning techniques could further enhance the adaptability of movement constraints in response to real-time operational conditions. A hybrid approach combining bio-inspired heuristics with learning-based methods may offer a robust solution for balancing proactive spatial constraint generation with real-time adaptability. Finally, additional validation in real industrial settings would help to verify the effectiveness of the method under actual operating conditions. This includes evaluating how the approach scales with larger fleets, interacts with dynamic obstacles such as human workers, and integrates with industrial fleet management systems.