Coordinated Multi-Robotic Vehicles Navigation and Control in Shop Floor Automation

In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation. It has convergent behavior from anywhere to the target and is computed in advance to increase computational efficiency. The novel optimization strategy used in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. Adaptive horizon length is used to improve performance. The efficiency of the proposed approaches is validated using simulations and experimental results.

Automated summary

This paper proposes a global navigation function for autonomous mobile robots in warehouse automation using model predictive control (MPC). The approach considers both static and dynamic obstacles, generating collision-free trajectories. The navigation function is derived from an E* graph search algorithm and bicubic interpolation on a discrete occupancy grid, with pre-computation for improved computational efficiency. The novel optimization strategy in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. The effectiveness of the proposed approaches is validated through simulations and experiments.