Cooperative Motion Planning for Multiple Autonomous Mining Trucks in Dumping Area of Open-pit Mines

Applications of autonomous mining trucks (AMTs) at open-pit mining sites are becoming a trend. Improving the driving efficiency of AMTs in dumping area enables them to quickly engage in the next loading task, thereby increasing mining productivity. In practice, this specific scenario presents typical challenges: 1) numerous path intersections and overlaps lead to severe AMT conflicts; 2) spatiotemporal conflicts are difficult to resolve and easy to cause deadlocks with increasing dumping demand; 3) the motion planning model is difficult to obtain solutions in real time. To resolve these problems, this study proposes a multi-AMT cooperative motion planning framework for dumping area, where the mine's cloud platform centrally computes terminal conditions for the speed planning of each AMT to coordinate conflicts, and each AMT performs speed planning in a distributed manner to ensure driving safety. In terms of centralized computing, a mixed-integer linear program (MILP) model is developed, and a grouped Monte Carlo tree search (GMCTS) model, inspired by connected component analysis in graph theory, is proposed to solve the MILP model within limited iterations. Simulation experiments of real dumping areas show that the proposed method allows to avoid deadlocks and enhance AMTs' operational efficiency within desirable computation time.