Efficient Trajectory Planning for Multiple Non-holonomic Mobile Robots via Prioritized Trajectory Optimization
This work provides an incremental improvement in trajectory planning efficiency for multi-robot systems, which is beneficial for applications requiring real-time operation in dynamic environments.
This paper addresses the challenge of efficiently planning collision-free, optimal trajectories for multiple non-holonomic mobile robots in complex environments. The authors developed a prioritized trajectory optimization method that significantly reduces computation time compared to coupled optimization, while maintaining near-optimal plan quality.
In this paper, we present a novel approach to efficiently generate collision-free optimal trajectories for multiple non-holonomic mobile robots in obstacle-rich environments. Our approach first employs a graph-based multi-agent path planner to find an initial discrete solution, and then refines this solution into smooth trajectories using nonlinear optimization. We divide the robot team into small groups and propose a prioritized trajectory optimization method to improve the scalability of the algorithm. Infeasible sub-problems may arise in some scenarios because of the decoupled optimization framework. To handle this problem, a novel grouping and priority assignment strategy is developed to increase the probability of finding feasible trajectories. Compared to the coupled trajectory optimization, the proposed approach reduces the computation time considerably with a small impact on the optimality of the plans. Simulations and hardware experiments verified the effectiveness and superiority of the proposed approach.