CL-MAPF: Multi-Agent Path Finding for Car-Like robots with kinematic and spatiotemporal constraints

Abstract

Multi-Agent Path Finding has been widely studied in the past few years due to its broad application in the field of robotics and AI. However, previous solvers rely on several simplifying assumptions. This limits their applicability in numerous real-world domains that adopt nonholonomic car-like agents rather than holonomic ones. In this paper, we give a mathematical formalization of the Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem. We propose a novel hierarchical search-based solver called Car-Like Conflict-Based Search to address this problem. It applies a body conflict tree to address collisions considering the shapes of the agents. We introduce a new algorithm called Spatiotemporal Hybrid-State A* as the single-agent planner to generate agents’ paths satisfying both kinematic and spatiotemporal constraints. We also present a sequential planning version of our method, sacrificing a small amount of solution quality to achieve a significant reduction in runtime. We compare our method with two baseline algorithms on a dedicated benchmark and validate it in real-world scenarios. The experiment results show that the planning success rate of both baseline algorithms is below 50% for all six scenarios, while our algorithm maintains that of over 98%. It also gives clear evidence that our algorithm scales well to 100 agents in 300 m × 300 m scenario and is able to produce solutions that can be directly applied to Ackermann-steering robots in the real world. The benchmark and source code are released in https://github.com/APRIL-ZJU/CL-CBS. The video of the experiments can be found on YouTube.