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

TL;DR

The paper addresses scalable, conflict-free MAPF for fleets of robots by replacing grid-based CBS with a structural-semantic topometric-map CBS (PM-CBS) framework. It generalizes vertex/edge conflicts to region ($\mathcal{RC}$) and opening ($\mathcal{OC}$) conflicts and introduces time-slot constraints, enabling continuous-time planning on topometric maps. A bounded-suboptimal extension (PM-ECBS) uses focal search at the high level to speed up computation, while a time-aware A* low-level planner handles region-time constraints. Validation includes benchmarking simulations showing improved success rates and reduced runtimes, and real-world TurtleBot3 experiments demonstrating practicality with high success and low latency. The approach offers a practical, scalable MAPF solution for real-world robotic fleets operating in structured environments, with potential for real-time deployment in complex indoor settings.

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.

Figures (6)

• Figure 1: Illustration of the region and opening conflicts in a structural semantic topometric map. In (a), $a_i$ enters a region already occupied by agent $a_j$. In (b), two agents are trying to go through the same opening at the same time.
• Figure 2: Maps used for the comparative study. Vertices/regions & edges/openings are marked with dots & lines respectively.
• Figure 3: Success rates in conflict-free multi-agent path finding of the proposed methods and traditional CBS approaches.
• Figure 4: Scaling of the computation time required by PM-CBS, PM-ECBS, CBS, and ECBS approaches with respect to the number of agents. The number of outliers that are outside the plot range is indicated on the top of each boxplot.
• Figure 5: The experimental validation of the proposed method is performed in a test area, with Turtlebots as the agents.