# Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems
## 2 Related Work
[20] developed an improved Q-learning approach for load balancing and traffic flow optimization using macroscopic fundamental diagrams. Furthermore, [PERSON] et al. [21] proposed a multi-agent deep deterministic policy gradient method for conflict-free path planning in container terminals, incorporating both centralized learning and distributed execution to coordinate multiple AGVs. However, these RL approaches face significant challenges in real-world deployment: they require extensive training data and computational resources, scale poorly with increasing fleet sizes, and primarily focus on individual path planning rather than generating system-wide movement rules. When optimizing for multiple objectives like throughput, energy efficiency, and collision avoidance, these methods often require prohibitively long training times and struggle to generalize across different environmental configurations. Because of their ability to handle complex dynamic environments and lower computational complexity, bio-inspired optimization strategies, such as Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and ACO, have gained a great deal of interest in robotic navigation. Even though the optimal solution is not guaranteed, these approaches are able to find sufficiently good solutions in a reasonable amount of time [22]. PSO approaches are inspired by the behavior of flocks of birds and have previously shown promise in tackling path planning [23; 24; 25] and task allocation [26]. GA approaches are based on the principles of natural selection, crossover, and mutation and have also been used for path planning problems for decades [22]. They have been used to generate optimal paths for robots in both static [27] and dynamic environments [28]. For generating efficient movement strategies, ACO algorithms, inspired by the foraging behavior of ants, have shown particular promise through their evolution of weighted path structures. A simple ACO algorithm for traffic flow improvement was demonstrated in [29], where directed weighted edges in a gridmap are iteratively optimized. The basic ACO approach was enhanced through various techniques to improve its performance. [PERSON] et al. [30] combined ACO with fuzzy control to address convergence speed issues, using critical obstacle influence factors and dynamic parameter adjustment to optimize single-robot path planning. [PERSON] et al. [31] applied ACO to simultaneous task allocation and path planning for AGVs, demonstrating its effectiveness in dynamic environments. [PERSON] et al. [32] and [PERSON] et al. [33] explored pheromone-based weight adaptation to balance path length with other constraints, such as safety and energy efficiency. Similarly, [PERSON] et al. [34] enhanced traditional ACO through time-varying pheromone distribution and optimized update strategies, although primarily focusing on single-robot applications. In traffic management applications, [PERSON] et al. [35] showed how ACO principles can dynamically adjust path costs to avoid congestion. [PERSON] and [PERSON] [36] introduced the idea of direction maps, which provide information about previous movements of agents and lead to implicit cooperation during movement and planning. This approach proved to be cheaper and more robust compared to explicit planning approaches. Despite these advances, several key limitations persist across the existing approaches. Traditional optimization methods like ARMO and expert systems require significant manual tuning and struggle with dynamic environments. While RL approaches show promise for holistic optimization, they face fundamental challenges in sample efficiency and computational scalability when dealing with large fleet sizes and multiple competing objectives. Bio-inspired methods, although computationally efficient, have primarily focused on single-agent path planning rather than generating system-wide movement constraints. Table 1 classifies the reviewed approaches for costmap/roadmap optimization in multi-robot systems, highlighting the key features of each method.