Layered LA-MAPF: a decomposition of large agent MAPF instance to accelerate solving without compromising solvability

Multi-Agent Path Finding (MAPF) has been widely studied in recent years. However, most existing MAPF algorithms assume that an agent occupies only a single grid in a grid-based map. This assumption limits their applicability in many real-world domains where agents have geometric shapes, rather than being point-like. Such agents, which can occupy multiple cells simultaneously, are referred to as ``large'' agents. When considering the shape and size of agents in MAPF, the computational complexity increases significantly as the number of agents grows, primarily due to the increased overhead in conflict detection between geometric agents. In this paper, we propose two types of subproblems for the LA-MAPF (Large-Agent MAPF) problem: \textbf{cluster} (which has no constraints on the order of solution) and \textbf{level} (which imposes constraints on the solution order). We introduce \textbf{Layered LA-MAPF}, a method that decomposes a MAPF instance involving geometric agents into clusters, and then further decomposes each cluster into levels. This approach aims to reduce time complexity when solving LA-MAPF problems. Our results demonstrate the performance of our method as the number of agents increases across various maps, and how it accelerates LA-MAPF methods, such as LA-CBS and LA-LaCAM. Experiments show that our LA-MAPF method with instance decomposition \textbf{halves the time cost (reducing from an average of 40s to 20s) and triples the success rate (from an average of 0.27 to 0.80)} in finding a solution within 60 seconds. To facilitate further research, we have made the source code for Layered LA-MAPF publicly available at \url{https://github.com/JoeYao-bit/LayeredMAPF/algorithm/LA-MAPF}.

LayeredMAPF: a decomposition of MAPF instance without compromising solvability

Generally, the calculation and memory space required for multi-agent path finding (MAPF) grows exponentially as the number of agents increases. This often results in some MAPF instances being unsolvable under limited computational resources and memory space, thereby limiting the application of MAPF in complex scenarios. Hence, we propose a decomposition approach for MAPF instances, which breaks down instances involving a large number of agents into multiple isolated subproblems involving fewer agents. Moreover, we present a framework to enable general MAPF algorithms to solve each subproblem independently and merge their solutions into one conflict-free final solution, without compromising on solvability. Unlike existing works that propose isolated methods aimed at reducing the time cost of MAPF, our method is applicable to all MAPF methods. In our results, we apply decomposition to multiple state-of-the-art MAPF methods using a classic MAPF benchmark (https://movingai.com/benchmarks/mapf.html). The decomposition of MAPF instances is completed on average within 1s, and its application to seven MAPF methods reduces the memory usage and time cost significantly, particularly for serial methods. To facilitate further research within the community, we have made the source code of the proposed algorithm publicly available (https://github.com/JoeYao-bit/LayeredMAPF).

An efficient tangent based topologically distinctive path finding for grid maps

Conventional local planners frequently become trapped in a locally optimal trajectory, primarily due to their inability to traverse obstacles. Having a larger number of topologically distinctive paths increases the likelihood of finding the optimal trajectory. It is crucial to generate a substantial number of topologically distinctive paths in real-time. Accordingly, we propose an efficient path planning approach based on tangent graphs to yield multiple topologically distinctive paths. Diverging from existing algorithms, our method eliminates the necessity of distinguishing whether two paths belong to the same topology; instead, it generates multiple topologically distinctive paths based on the locally shortest property of tangents. Additionally, we introduce a priority constraint for the queue during graph search, thereby averting the exponential expansion of queue size. To illustrate the advantages of our method, we conducted a comparative analysis with various typical algorithms using a widely recognized public dataset\footnote{https://movingai.com/benchmarks/grids.html}. The results indicate that, on average, our method generates 320 topologically distinctive paths within a mere 100 milliseconds. This outcome underscores a significant enhancement in efficiency when compared to existing methods. To foster further research within the community, we have made the source code of our proposed algorithm publicly accessible\footnote{https://joeyao-bit.github.io/posts/2023/09/07/}. We anticipate that this framework will significantly contribute to the development of more efficient topologically distinctive path planning, along with related trajectory optimization and motion planning endeavors.