Kinematically feasible path planning and robust predefined performance formation control for MAGVs under mixed uncertainties

Conventional path planning methods often suffer from low computational efficiency in unknown environments and frequently neglect the kinematic and dynamic feasibility of the generated paths. To address these challenges, this paper proposes a hierarchical planner-controller framework for multi-automated guided vehicles (MAGVs) that integrates optimal path planning with robust formation control subject to mixed uncertainties. Firstly, a novel hybrid Q-learning (HQL) algorithm is developed, which employs a distance-based initialization strategy, a Dubins-curve smoothing mechanism and the adaptive exploration rate to significantly accelerate convergence and ensure kinematic feasibility. Secondly, an adaptive fixed-time predefined performance controller (AFTPPC) is designed for the physical control layer. By introducing distributed fixed-time estimators and logarithmic error transformation mechanisms, it enables the controllers of all AGVs to ensure that the tracking error strictly converges within the specified range within a fixed time, regardless of the initial conditions or unmodeled dynamics. Simulation results demonstrate the framework’s superior robustness and efficiency, reducing the average path length by 29.4% and ensuring tracking errors converge within 0.5s, thereby maintaining high precision throughout the operation despite external disturbances.