Parking Trajectory Planning for Autonomous Mining Trucks: A Global Optimal Method Based on Divide-and-Conquer Strategy

Parking trajectory planning in loading/dumping areas of open-pit mines presents unique challenges compared to standard parking scenarios for passenger vehicles. These include irregular workspaces, kinematic constraints of heavy trucks, and customized trajectory requirements, which collectively formulate a mixed-integer nonlinear programming (MINLP) problem with non-differentiable logical condition constraints. Mainstream MINLP solvers struggle to directly handle logical conditional constraints involving integer variables and usually fail to meet the real-time requirements of practical applications. This study presents a nearly global optimal trajectory planning method that developed a search-then-optimize framework based on the divide-and-conquer strategy, separating the difficulties into distinct stages. A multiple-priority-queue search-based method is introduced in the search stage, leveraging its gradient-free nature to tackle non-differentiable variables and generate an initial solution near the global optimal solution. In the numerical optimization stage, the problem is reformulated as a fully differentiable NLP, refining the initial trajectory to enhance quality. Extensive simulations and real-vehicle tests confirm that this method effectively addresses the above difficulties, producing efficient and trackable trajectories.