Collision-Free Control for Autonomous Industrial Vehicles: A Novel Multidimensional Stability and Learning-Based MPC Hierarchical Scheme

Active collision avoidance is a crucial feature for autonomous driving of industrial vehicles, which requires the vehicles to navigate around obstacles while maintaining its multidimensional stability within a limited distance. To satisfy the requirements of safety and transportation efficiency simultaneously, this paper proposes a novel hierarchical control scheme based on a redefined collision avoidance distance and the modified stable handling envelope. First, in the upper decision-making level, the safety distance and minimum collision avoidance distance are determined subject to the multi-dimensional stability of the industrial vehicle. Then two collision avoidance cases are identified. In the first case, the reference acceleration is generated based on the modified stable handling envelope and the boundaries are also used for stability control. In the second case, collision-free path and roll stability limits are defined for path tracking. Second, in the lower motion-tracking level, to handle model uncertainties and external disturbances, a learning-based model predictive control using Gaussian process regression is designed based on the revised stability envelopes and boundaries. Finally, the effectiveness and superiority of the proposed control framework are demonstrated in various emergency scenarios. The results indicate that the control scheme effectively maintains the stability of the vehicle and improves transportation efficiency while avoiding obstacles.