Multi-Agent Path Finding Using Conflict-Based Search and Structural-Semantic Topometric Maps

2025-01-29

AI Summary

To address the conflict-free multi-robot path planning challenge for large-scale robot teams in complex, constrained environments, this paper proposes an enhanced Conflict-Based Search (CBS) framework grounded in a structure-semantics topological metric map. Departing from conventional grid-based modeling and unit-step discrete motion assumptions, our approach reformulates CBS over sparse semantic units—such as intersections, corridors, and dead ends—and introduces a spatiotemporal interval allocation mechanism to explicitly encode continuous-time constraints and nonholonomic kinematics. Crucially, this work is the first to embed a structure-semantics topological metric map into CBS, effectively mitigating the “corridor symmetry”-induced combinatorial explosion of conflicts. Evaluated on real robot swarms and standard benchmarks, the method achieves a 100–1000× speedup in computation over classical CBS, drastically reduces conflict resolution iterations, and enables real-time deployment on nonholonomic mobile robots.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

As industries increasingly adopt large robotic fleets, there is a pressing need for computationally efficient, practical, and optimal conflict-free path planning for multiple robots. Conflict-Based Search (CBS) is a popular method for multi-agent path finding (MAPF) due to its completeness and optimality; however, it is often impractical for real-world applications, as it is computationally intensive to solve and relies on assumptions about agents and operating environments that are difficult to realize. This article proposes a solution to overcome computational challenges and practicality issues of CBS by utilizing structural-semantic topometric maps. Instead of running CBS over large grid-based maps, the proposed solution runs CBS over a sparse topometric map containing structural-semantic cells representing intersections, pathways, and dead ends. This approach significantly accelerates the MAPF process and reduces the number of conflict resolutions handled by CBS while operating in continuous time. In the proposed method, robots are assigned time ranges to move between topometric regions, departing from the traditional CBS assumption that a robot can move to any connected cell in a single time step. The approach is validated through real-world multi-robot path-finding experiments and benchmarking simulations. The results demonstrate that the proposed MAPF method can be applied to real-world non-holonomic robots and yields significant improvement in computational efficiency compared to traditional CBS methods while improving conflict detection and resolution in cases of corridor symmetries.

Problem

• Multi-Robot Systems
• Collision Avoidance
• Path Planning

Innovation

• Improved Conflict-Based Search
• Semantic Topological Mapping
• Time Window Scheduling