How to track evaders with multiple robots?
FG-PE: Factor-graph Approach for Multi-robot Pursuit-Evasion
This paper proposes FG-PE, a novel factor graph approach for solving the multi-robot pursuit-evasion problem. It uses factor graphs to represent relationships between pursuers, evaders, and the environment, enabling efficient probabilistic reasoning for estimating evader position and planning pursuer movements under uncertainty.
Relevant to LLM-based multi-agent systems are the concepts of: using factor graphs for relationship representation and reasoning in multi-agent scenarios; handling uncertainty in predictions and measurements, crucial for real-world applications where LLMs provide probabilistic outputs; and the demonstrated effectiveness of this approach in minimizing uncertainty and optimizing multi-agent collaboration for a shared goal. The focus on message passing within the graph structure also suggests potential for integrating LLMs as communication interfaces between agents.