# Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems
## 1 Introduction  
Setting up efficient multi-robot systems in industrial environments presents a complex challenge. In industrial settings, this requires careful consideration of numerous interconnected parameters, such as plant layout, task distribution, dispatching, and pathfinding algorithms [1]. These parameters form a web of dependencies where adjustments to one aspect often necessitate changes in others to maintain the overall system efficiency and performance [2, 3]. The specific characteristics of each intralogistic problem demand strategically developed movement constraints to prevent collisions, deadlocks,and congestion--common challenges that impact system performance in warehouse and manufacturing environments [4; 5]. While various terms are used in the literature to describe movement rules for mobile robots, this paper focuses on movement constraints and spatial rules that influence robot navigation decisions, implemented through costmaps in the form of weighted directed grid graphs. This implementation provides more flexibility than traditional roadmaps (physical or virtual predefined paths) while maintaining structured traffic patterns. Traditionally, intralogistic automation has relied on Autonomous Guided Vehicles (AGVs), which require predefined paths marked by magnetic tapes or similar guidance systems. These roadmaps must be carefully designed by human experts with a deep understanding of system operations [6]. While effective, this manual design process can be extremely time-consuming, leading to increased research interest in automated approaches that can accelerate the process and improve the solution quality [7; 8; 9; 10]. In recent years, autonomous mobile robots (AMRs) have emerged as a flexible alternative to AGVs, offering the ability to navigate anywhere within the workspace [11]. However, this increased flexibility does not automatically translate to higher efficiency. In narrow corridors or aisles, unrestricted AMR movement can lead to congestion and reduced throughput [12]. Similarly, in high-density storage areas, the lack of structured movement can result in inefficient navigation and increased collision risks [13]. Thus, while AMRs do not require physical guidance infrastructure, they still benefit from structured movement patterns that help to prevent or resolve conflicts efficiently. To address these challenges while maintaining AMRs' inherent flexibility, we propose an approach based on ant colony optimization (ACO) for the automated generation of movement constraints. The method utilizes a weighted, directed grid graph where the edge weights are dynamically adjusted according to real-time performance metrics. The approach combines offline costmap generation through simulation-based optimization with subsequent integration into path planning systems. During simulation, the algorithm explores the workspace under various operational conditions, utilizing digital pheromones to represent traversal costs, congestion levels, and collision risks. These pheromone distributions are aggregated into precomputed costmaps that provide a foundation for global path planning. The resulting costmaps can be integrated with established planning algorithms such as A*, effectively guiding robots toward more efficient collective movement patterns. The optimization algorithm implements four mechanisms derived from ant colony systems. These include movement rewards that reinforce successful paths, delay penalties that modify weights based on task completion times, collision penalties that adjust weights in conflict areas, and an evaporation mechanism that prevents convergence to suboptimal solutions. In contrast to existing approaches that consider only the physical layout, our method incorporates key intralogistic system parameters including fleet size, task distribution patterns, dispatching algorithms, and operational constraints.