How can I optimize multi-agent coverage in complex environments?
NavEX: A Multi-Agent Coverage in Non-Convex and Uneven Environments via Exemplar-Clustering
This paper introduces NavEX, a new method for deploying multiple agents (e.g., robots, sensors) in complex environments with obstacles and uneven terrain. It aims to solve two problems: fair-access deployment (minimizing the maximum distance between any target and its nearest agent) and hotspot deployment (placing agents in densely populated or high-demand areas).
Key points for LLM-based multi-agent systems: NavEX uses flexible distance metrics calculated by visibility graphs (2D) or traversability-aware RRT* (3D) and incorporates exemplar-clustering, a data summarization technique, to define the utility function for optimization. This approach enables the use of non-Euclidean distances and allows LLMs to reason about complex spatial relationships in environments with obstacles, potentially enhancing agent navigation and coordination in simulated or real-world scenarios. Furthermore, the flexibility of the distance metric and its integration with a submodular optimization framework offer potential avenues for combining with LLMs that can learn and adapt to new or changing environmental conditions.