Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Movable Obstacles

2025-08-31

AI Summary

This paper addresses the Block Rearrangement Problem (BRaP) in high-density warehouse environments—rearranging stored blocks into a target layout within a dense grid by moving obstructing blocks. To tackle the combinatorial explosion arising from deeply buried target blocks, we propose a formal BRaP model that unifies multi-agent pathfinding (MAPF), symbolic planning, and sliding-puzzle heuristics within a joint configuration space for efficient search. We design five novel search algorithms that balance completeness and scalability. Experiments on grids up to 80×80 demonstrate substantial improvements in both solution quality and computational efficiency, overcoming the exponential-state-space limitations of conventional approaches. Our framework establishes a new paradigm for real-time, large-scale block rearrangement in automated warehousing systems.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Motion and Path Planning

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a target state. We formally define the BRaP as a graph search problem. Building on intuitions from sliding puzzle problems, we propose five search-based solution algorithms, leveraging joint configuration space search, classical planning, multi-agent pathfinding, and expert heuristics. We evaluate the five approaches empirically for plan quality and scalability. Despite the exponential relation between search space size and block number, our methods demonstrate efficiency in creating rearrangement plans for deeply buried blocks in up to 80x80 grids.

Problem

• Solving block rearrangement in dense warehouse grids
• Developing efficient algorithms for movable obstacles
• Scaling solutions to large 80x80 grid environments

Innovation

• Graph search formulation for block rearrangement
• Five search algorithms combining multiple techniques
• Efficient solutions for high-density 80x80 grids