Abstract: In the cooperative multi-agent pathfinding and motion planning, given a unique start position and a unique goal position for each agent, all agents are able to pursue their own goals without colliding with each other. To aim at realizing the collision-free motion of the agents within the tractable time, this work proposes a polynomial-time solver, called the HBD-AOI, hybridizing centralized and decentralized schemes. Firstly, an algorithm of centralized pathfinding is utilized to plan the optimal paths of all agents. Afterwards, each of the agents updates the local motion pattern to tracks its own planned waypoints with the obstacle avoidance in a decentralized manner. Furthermore, to resolve unavoidable egoistic conflicts occurring in the decentralized scheme, a centralized intervener with the route replanning is invoked to coach the involved agents to abort the existing deadlocks. Bounded by an amount of time, the performances of the proposed and benchmarked algorithms are simulated on the same instance, from the evaluated testbeds that consists of various maps and scenarios. In the simulations, it is proved that this work outperforms other benchmarked algorithms for all presented instances in the term of the success rate. The experimental results are also demonstrated to verify the feasibility of the proposed methodology.
