How can I build robust multi-robot coordination with uncertain locations?
Distributed Linear Quadratic Gaussian for Multi-Robot Coordination with Localization Uncertainty
This paper proposes a new control algorithm for coordinating multiple robots to meet at a single point, even when their location information is noisy. It uses an approach called Linear Quadratic Gaussian (LQG) control, which is good at handling uncertainty.
The key points relevant to LLM-based multi-agent systems are the distributed nature of the control algorithm and its robustness to noise. Each robot makes decisions independently based on noisy data, yet they still manage to coordinate effectively. This decentralized and robust decision-making under uncertainty is highly relevant to the challenges faced in developing real-world LLM-based multi-agent applications. The use of an optimization framework (minimizing a cost function) could also be applied in LLM-based systems to achieve desired behaviors while minimizing resource usage.