# Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems
## 4 Case Studies  
To validate our approach, we tested the optimization algorithm on three distinct environments: a standard MAPF benchmark layout featuring connected rooms and two real industrial scenarios from manufacturing facilities. For the industrial scenarios, we compared our results with solutions from human experts to evaluate their performance against traditional manual design approaches. These cases were chosen to demonstrate the effectiveness of the method across different scales and levels of complexity, from academic benchmarks to real-world applications.  
Given the unique nature of our approach, which focuses on spatial optimization through movement constraints rather than temporal coordination as in traditional MAPF, a direct comparison with the existing benchmarks is challenging. While MAPF benchmarks typically evaluate collision avoidance and temporal efficiency, our method addresses the spatial distribution of robot traffic and the associated costs. Therefore, we evaluated our approach in two ways: first by comparing it to human expert solutions in real industrial scenarios and second by measuring the improvement over standard A* pathfinding without directional constraints. This evaluation methodology enables demonstrating both the practical value in real-world settings and the quantitative benefits of our spatial optimization approach.  
The expert solutions were designed by industry practitioners who were provided the layout, number of robots, and task descriptions, including frequencies and pickup/delivery locations. The experts manually drew their preferred directions on paper, which were then translated into low directed costs for comparison with our automated approach. While expert-designed solutions benefit from human intuition and experience, they face several inherent limitations. Experts typically rely on simple rules and visual heuristics, making it difficult to optimize for complex interactions between multiple robots. They tend to favor symmetric patterns that are easier to conceptualize but may not be optimal. Additionally, it is difficult for experts to take into account the specifics of an intralogistic problem such as specific task frequencies between stations.  
The evaluation process included both quick assessments and comprehensive validations to ensure the robustness of our optimization algorithm. First, we used the optimization algorithm to generate movement constraints for each environment using the event-based simulator. The solutions were then evaluated within the same environment. We then performed a thorough validation of the optimized movement constraints using physics-based simulations in Gazebo. In this stage, the constraints were implemented through our custom Nav2 global planner plugin, which enabled a detailed examination of the algorithm's performance in a more realistic simulation environment.  
All the optimizations were performed with the same parameter settings as described in Section 3, with the only variation being the number of AMRs, which was adjusted according to the specific requirements of each environment. This consistency helps to demonstrate the robustness of the method across different scenarios while also illustrating how the resulting movement patterns naturally adapt to the unique characteristics of each layout.