How to optimize robot coverage with varying energy levels?
Energy-Aware Coverage Planning for Heterogeneous Multi-Robot System
This paper addresses energy-efficient coverage planning for a heterogeneous team of robots, where robots have varying energy capacities and depletion rates. It proposes a distributed control algorithm (EAC) that dynamically assigns coverage areas to robots based on their individual energy dynamics. Robots with higher energy capacity or lower depletion rates are assigned larger areas, optimizing overall mission duration and coverage quality.
For LLM-based multi-agent systems, this research is relevant as it provides a mechanism for dynamically allocating tasks (analogous to coverage areas) among agents with varying computational resources or "energy" constraints. EAC's distributed nature is also applicable to decentralized multi-agent systems common with LLMs, where agents can make local decisions based on information shared with neighbors. The concept of adapting task allocation based on real-time resource availability is directly transferable to managing LLM inference costs and optimizing performance in resource-constrained environments.