Ant-Colony-Inspired Grid Graph Optimization for Improving Logistic Performance of Multi-AMR Systems

Autonomous mobile robots (AMRs) are increasingly used in intralogistic applications. To efficiently set up a multi-AMR system, numerous parameters such as plant layout, task distribution, dispatching, and pathfinding algorithms must be considered. Based on these characteristics, an appropriate number of AMRs must be selected and movement constraints, i.e., preferred directions of motion, must be established. In this paper, we propose an ant-colony inspired approach for generating movement constraints in the form of a weighted grid graph that minimize conflicts between AMRs. By analysing the system throughput in simulation, we also propose the optimal number of AMRs for a specific intralogistic problem. The proposed approach provides a more efficient and conflict-free movement strategy that ultimately improves the performance of multi-AMR systems.