露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈.为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法.首先,将决策模型建模为混合整数线性规划(Mixed Integer Linear Programming,MILP)模型,表述优化目标和问题约束;其次,考虑到求解MILP模型存在难以满足动态决策实时性的问题,基于蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)实现多车冲突消解,核心思想是利用搜索树的推演能力进行多车通行前瞻模拟,计算多车的最优通行优先级,动态调整多车的可行驶距离;此外,根据无人矿车在作业区内的作业特征设计不同的MCTS节点价值函数,实现综合考虑运输效率与作业特征的通行优先级排序;最后,设计作业区4,8,12个停车位场景下的多车通行仿真实验,与基于先到先服务(First-Come-First-Served,FCFS)的方法进行对比,吞吐量提升22.03%～28.00%,平均停车等待时间缩短31.71%～50.79%.同时,搭建微缩智能车辆的6停车位作业区场景实验平台,多车单次运输作业总用时相比FCFS缩短了18.84%.仿真与微缩智能车辆的实验结果表明,本文提出的方法能够提升露天矿作业区多车运输效率.
Collaborative Driving Decision-making Method of Unmanned Mining Trucks in Open-pit Mine Operation Areas
The long parking and waiting time of unmanned mining trucks in open-pit mines during transportation in the loading and unloading operation area is a bottleneck that restricts the efficiency improvement of unmanned transportation systems in open-pit mines.To improve the transportation efficiency of unmanned mining trucks,this paper combines the transportation operation process in the operation area and proposes a multi-vehicle collaborative driving decision-making method based on dynamic travelable distance.The decision-making model was formulated as a mixed integer linear programming(MILP)model to express the optimization objective and problem constraints.Considering the challenge of meeting real-time decision-making requirements in solving the MILP model,the multi-vehicle conflict resolution was implemented based on Monte Carlo tree search(MCTS).The core idea was to use the derivation capability of the search tree to conduct forward simulation of multi-vehicle driving,calculate the optimal driving priority of multi-vehicle,and thereby dynamically adjust the travelable distance of multi-vehicle.In addition,different MCTS node value functions were designed based on the operating characteristics of unmanned mining trucks in the operation area to achieve driving priority ranking that comprehensively considered transportation efficiency and operating characteristics.A multi-vehicle driving simulation experiment was designed in the scenario of 4,8,and 12 parking spots in the operation area.Compared with the method based on first-come-first-served(FCFS),the throughput was increased by 22.03%to 28.00%and the average parking waiting time was shortened by 31.71%to 50.79%.In addition,a 6-parking spots operation area scenario experimental platform for miniature intelligent vehicles was built.The total multi-vehicle single-operation time was reduced by 18.84%compared to the FCFS.The results of simulation and miniature intelligent vehicles experiments indicated that the proposed method could enhance the efficiency of multi-vehicle transportation in open-pit mine operation areas.
intelligent transportationcollaborative driving decision-makingMonte Carlo tree searchunmanned mining trucksdynamic travelable distanceopen-pit mine operation areas
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