Distributed Multi-Vehicle Task Assignment and Motion Planning in Dense Environments

This article investigates the multi-vehicle task assignment and motion planning (MVTAMP) problem. In a dense environment, a fleet of non-holonomic vehicles is appointed to visit a series of target positions and then move to a specific ending area for real-world applications such as clearing threat targets, aid rescue, and package delivery. We presented a novel hierarchical method to simultaneously address the multiple vehicles’ task assignment and motion planning problem. Unlike most related work, our method considers the MVTAMP problem applied to non-holonomic vehicles in large-scale scenarios. At the high level, we proposed a novel distributed algorithm to address task assignment, which produces a closer to the optimal task assignment scheme by reducing the intersection paths between vehicles and tasks or between tasks and tasks. At the low level, we proposed a novel distributed motion planning algorithm that addresses the vehicle deadlocks in local planning and then quickly generates a feasible new velocity for the non-holonomic vehicle in dense environments, guaranteeing that each vehicle efficiently visits its assigned target positions. Extensive simulation experiments in large-scale scenarios for non-holonomic vehicles and two real-world experiments demonstrate the effectiveness and advantages of our method in practical applications. The source code of our method can be available at https://github.com/wuuya1/LRGO. Note to Practitioners —The motivation for this article stems from the need to solve the multi-vehicle task assignment and motion planning (MVTAMP) problem for non-holonomic vehicles in dense environments. Many real-world applications exist, such as clearing threat targets, aid rescue, and package delivery. However, when vehicles need to continuously visit a series of assigned targets, motion planning for non-holonomic vehicles becomes more difficult because it is more likely to occur sharp turns between adjacent target path nodes. In this case, a better task allocation scheme can often lead to more efficient target visits and save all vehicles’ total traveling distance. To bridge this, we proposed a hierarchical method for solving the MVTAMP problem in large-scale complex scenarios. The numerous large-scale simulations and two real-world experiments show the effectiveness of the proposed method. Our future work will focus on the integrated task assignment and motion planning problem for non-holonomic vehicles in highly dynamic scenarios.