How can I efficiently compress maps for robot communication?
Communication-aware Hierarchical Map Compression of Time-Varying Environments for Mobile Robots
This paper tackles the challenge of compressing map data for transmission between robots in a multi-agent system, particularly when communication bandwidth is limited. It formulates an optimization problem to find the best compression that balances map quality (low distortion) with size, leveraging a hierarchical tree structure (like a quadtree) to represent the map at different levels of detail. The approach also explicitly addresses how the receiving robot uses the compressed information to update its map estimate, especially in dynamic environments where the map changes over time due to moving obstacles.
Key points for LLM-based multi-agent systems: The hierarchical map representation could be useful for LLMs to reason about environments at different levels of abstraction, enabling efficient communication and decision-making. The focus on dynamic environments and map updates is relevant for real-world multi-agent scenarios where LLMs need to adapt to changing information. The optimization framework for balancing information quality and communication cost could be adapted for LLM-based communication protocols. The innovation-based compression, focusing on changes in the map rather than the whole map, aligns with how LLMs might process and share new information.