Abstract
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.
Highlights
Multi-robot systems play a vital role in next-generation factories, urban search and rescue, and package delivery, and they are anticipated to be applied in space exploration [1,2].One of the key ingredients to a multi-robot system is the motion planning module
The double-layer corridors are applied as safety constraints of model predictive priority contouring control
The low-level planner of the conflicted-based search (CBS) planner is replaced by the hybrid A* planner
Summary
Multi-robot systems play a vital role in next-generation factories, urban search and rescue, and package delivery, and they are anticipated to be applied in space exploration [1,2]. To deal with the MMP problem of non-holonomic robots, a two-stage pipeline, i.e., reference path planning and trajectory generation, is applied in a series of works [9,10,11]. As this method only considers local obstacle avoidance, the global performance is poor and may lead the robot into deadlock Another well-known decentralized method is distributed model predictive control (DMPC) [4,16,17]. In [17], Luis et al proposed a distributed model predictive control (DMPC) method for multi-robot motion planning to generate trajectories in real-time. The double-layer corridors are applied as safety constraints of model predictive priority contouring control. Model predictive priority contouring control with the double-layer corridors is proposed to generate multi-robot trajectories, which ensures security while greatly improving computational efficiency. In. Section 4, the model predictive priority contouring control with double-layer corridors is proposed.
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