How to best coordinate data-gathering agents?
Multi-agent coordination for on-demand data gathering with periodic information upload
March 17, 2025
https://arxiv.org/pdf/2503.11504This paper proposes a method for coordinating a team of robots (agents) to gather data from dynamically changing locations and upload it to a central operation center (OC). The system balances data freshness and the amount of data delivered, considering communication limitations. Agents are divided into workers (gather data) and collectors (relay data to the OC). A three-step method determines efficient worker zones, the optimal number of collectors and their routes, and synchronized data transfer between workers and collectors/OC.
Key points for LLM-based multi-agent systems:
- Dynamic task allocation: Goals change each cycle, demanding a system adaptable to shifting requirements. LLMs could be used for higher-level reasoning and decision-making about task assignments and role allocation based on real-time data.
- Role assignment: The worker/collector distinction reflects potential agent specialization within a larger multi-agent system. LLMs could dynamically determine roles based on individual agent strengths and weaknesses, potentially leading to more efficient task completion.
- Connectivity constraints: Limited communication range poses a challenge for coordination. LLMs could assist agents in negotiating data exchange points and times, optimizing information flow despite constraints.
- Path planning and synchronization: Efficient pathfinding and synchronization are crucial. LLMs could be used to reason about resource allocation and time management, enabling agents to make more informed decisions about their actions.
- Centralized planning, distributed execution: While planning happens centrally, the execution is distributed among agents. This aligns with a potential LLM-driven system where LLMs handle the "thinking" while agents handle the "doing."