How can I improve robot pathfinding in complex environments?
Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search
This paper introduces MASS, a new algorithm for coordinating the movement of multiple robots, specifically differential drive robots (like those used in warehouses), within a shared space, ensuring they don't collide and reach their destinations efficiently. It uses a three-tiered approach: a high-level planner to avoid collisions between robots, a mid-level planner to generate individual robot paths considering their movement limitations (e.g., they can only rotate when stationary), and a low-level planner to optimize the speed and acceleration of each robot along its path. The method is also adapted for long-term, continuous operation where robots receive new goals over time.
While not directly addressing LLMs, MASS's hierarchical planning structure, focus on dynamic constraints, and adaptation to lifelong tasks are relevant to LLM-based multi-agent systems. The concept of breaking down complex multi-agent problems into hierarchical levels and focusing on action sequences with constraints parallels the challenges in managing multiple LLM agents collaborating on a shared task. Furthermore, the adaptive windowing approach in MASS for lifelong tasks offers insights into designing LLM-based agents that can continuously adapt to evolving goals and environmental changes.