How can robots agree & explore most efficiently?
Unification of Consensus-Based Multi-Objective Optimization and Multi-Robot Path Planning
This paper explores coordinating multiple lunar rovers for efficient exploration. One "leader" rover follows an optimized path, while the others autonomously follow using an algorithm that adjusts their headings based on the leader's movements and tuned communication weights. The system optimizes both area coverage and how quickly the rover group converges to the same heading.
For LLM-based multi-agent systems, this research demonstrates how to combine a pre-planned optimal path with reactive, autonomous follower agents. The tunable communication weights and iterative heading adjustments offer a model for coordinating agents with varying levels of autonomy, similar to LLMs directing simpler agents. The focus on maximizing explored area is relevant to information gathering tasks in multi-agent LLM applications.