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Disturbance poses a major challenge for the safety and real-time performance of robust robot motion planning. To address the disturbance while improving the real-time performance of multi-robot robust motion planning, a model predictive priority contouring control method is proposed. First, an improved conflict-based search (ICBS) planner is utilized to plan reference paths. The low-level planner of the conflicted-based search (CBS) planner is replaced by the hybrid A* planner and reference paths are adopted as an initial guess of model predictive priority contouring control. Second, double-layer corridors are proposed to provide safety guarantees, which include static-layer corridors and dynamic-layer corridors. The static-layer corridors are generated based on reference paths and the dynamic-layer corridors are generated based on the relative positions and velocities of robots. The double-layer corridors are applied as safety constraints of model predictive priority contouring control. Third, a prioritization mechanism is devised to improve computational efficiency. Priorities are assigned according to each robot’s task completion percentage. Based on the assigned priority, multiple robots are grouped, and each group executes the model predictive priority contouring control algorithm to acquire trajectories. Finally, our method is compared with the centralized method and the soft constraint-based DMPC. Simulations verify the effectiveness and real-time performance of our approach.

Multi-Robot Robust Motion Planning based on Model Predictive Priority Contouring Control with Double-Layer Corridors