How can robots find many sources efficiently?
Distributed Multi-robot Source Seeking in Unknown Environments with Unknown Number of Sources
This paper introduces DIAS, a distributed multi-robot system for locating multiple unknown sources (e.g., gas leaks) where the number of sources may exceed the number of robots. DIAS uses Voronoi tessellation to divide the search space, Gaussian Process Regression to model the source distribution, and a hybrid controller that balances exploration (gathering data) and exploitation (moving towards potential sources).
Key points for LLM-based multi-agent systems: DIAS's hybrid control approach, balancing exploration and exploitation, is highly relevant to decision-making in multi-agent LLM applications. The use of Gaussian Processes to model an unknown environment could be adapted to represent uncertain information states within an LLM-based system. The distributed nature of DIAS, relying on local information and limited communication, aligns well with the challenges of scaling multi-agent LLM systems. DIAS's ability to identify all sources, even when outnumbered, is valuable for LLM agents needing to exhaustively consider multiple options or perspectives.