露天矿作业区无人矿车协同通行决策方法研究

doi:10.16097/j.cnki.1009-6744.2024.03.027

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (3): 277-289.DOI: 10.16097/j.cnki.1009-6744.2024.03.027

露天矿作业区无人矿车协同通行决策方法研究

倪浩原^{1a, 1b}，余贵珍^{1a, 1b}，李涵^{1a, 1b}，陈鹏^{*1a, 1b}，刘喜²，王文达²

1. 北京航空航天大学，a. 交通科学与工程学院，b. 特种车辆无人运输技术工业和信息化部重点实验室，北京 100083； 2. 国能北电胜利能源有限公司，内蒙古锡林浩特 026000

收稿日期:2023-10-19 修回日期:2024-01-26 接受日期:2024-04-18 出版日期:2024-06-25 发布日期:2024-06-24
作者简介:倪浩原(1997- )，男，吉林人，博士生
基金资助:
国家重点研发计划(2022YFB4703702)

Collaborative Driving Decision-making Method of Unmanned Mining Trucks in Open-pit Mine Operation Areas

NI Haoyuan^{1a, 1b}, YU Guizhen^{1a, 1b}, LI Han^{1a, 1b}, CHEN Peng^{*1a, 1b} , LIU Xi² , WANG Wenda²

1a. School of Transportation Science and Engineering, 1b. Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, Beihang University, Beijing 100083, China; 2. Guoneng Nortel Shengli Energy Co Ltd, Xilin Gol 026000, Inner Mongolia, China

Received:2023-10-19 Revised:2024-01-26 Accepted:2024-04-18 Online:2024-06-25 Published:2024-06-24
Supported by:
National Key Research and Development Program of China (2022YFB4703702)

摘要/Abstract

摘要： 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-FirstServed, 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。

关键词: 智能交通, 协同通行决策, 蒙特卡洛树搜索, 无人矿车, 动态可行驶距离, 露天矿作业区

Abstract: 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 multivehicle 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.

Key words: intelligent transportation, collaborative driving decision-making, Monte Carlo tree search, unmanned mining trucks, dynamic travelable distance, open-pit mine operation areas

中图分类号:

U491

倪浩原, 余贵珍, 李涵, 陈鹏, 刘喜, 王文达. 露天矿作业区无人矿车协同通行决策方法研究[J]. 交通运输系统工程与信息, 2024, 24(3): 277-289.

NI Haoyuan, YU Guizhen, LI Han, CHEN Peng, LIU Xi, WANG Wenda. Collaborative Driving Decision-making Method of Unmanned Mining Trucks in Open-pit Mine Operation Areas[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(3): 277-289.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2024.03.027

http://www.tseit.org.cn/CN/Y2024/V24/I3/277

参考文献

[1] 陈坚, 李睿, 傅志妍. 基于 UTAUT 的无人驾驶公交乘客接受度模型[J]. 交通运输系统工程与信息, 2019, 19 (6): 38-44. [CHEN J, LI R, FU Z Y. Model of acceptance of unmanned buses based on UTAUT[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(6): 38-44.]

[2] 张佩瑜, 周建山, 田大新. 网联车辆编队的鲁棒模型预测控制方法[J]. 交通运输系统工程与信息, 2022, 22 (4): 268-274. [ZHANG P Y, ZHOU J S, TIAN D X. Robust model predictive control method for connected vehicle platoon[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(4): 268-274.]

[3] 孔国杰, 冯时, 于会龙, 等. 无人集群系统协同运动规划技术综述[J]. 兵工学报, 2023, 44(1): 11-26. [KONG G J, FENG S, YU H L, et al. A review on cooperative motion planning of unmanned vehicles[J]. Acta Armamentarii, 2023, 44(1): 11-26.]

[4] YUAN M, SHAN J, MI K. Deep reinforcement learning based game-theoretic decision-making for autonomous vehicles[J]. IEEE Robotics and Automation Letters, 2021, 7(2): 818-825.

[5] FAJARDO D, AU T C, WALLER S T, et al. Automated intersection control: Performance of future innovation versus current traffic signal control[J]. Transportation Research Record, 2011, 2259(1): 223-232.

[6] DING J, LI L, PENG H, et al. A rule-based cooperative merging strategy for connected and automated vehicles [J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(8): 3436-3446.

[7] 郝威, 龚雅馨, 张兆磊, 等. 面向高速公路混合交通流的车辆协同合流策略[J]. 交通运输系统工程与信息, 2023, 23(1): 224-235. [HAO W, GONG Y X, ZHANG Z L, et al. Cooperative merging strategy for freeway ramp in a mixed traffic environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 224-235.]

[8] XU H, ZHANG Y, LI L, et al. Cooperative driving at unsignalized intersections using tree search[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(11): 4563-4571.

[9] FAYAZI S A, VAHIDI A. Mixed-integer linear programming for optimal scheduling of autonomous vehicle intersection crossing[J]. IEEE Transactions on Intelligent Vehicles, 2018, 3(3): 287-299.

[10] LI J, RAN M, XIE L. Efficient trajectory planning for multiple non-holonomic mobile robots via prioritized trajectory optimization[J]. IEEE Robotics and Automation Letters, 2020, 6(2): 405-412.

[11] TIAN F, ZHOU R, LI Z, et al. Trajectory planning for autonomous mining trucks considering terrain constraints [J]. IEEE Transactions on Intelligent Vehicles, 2021, 6 (4): 772-786.

[12] MIRHELI A, HAJIBABAI L, HAJBABAIE A. Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment[J]. Transportation Research Part C: Emerging Technologies, 2018, 92: 412-425.

[13] LI L, WANG F Y. Cooperative driving at blind crossings using intervehicle communication[J]. IEEE Transactions on Vehicular Technology, 2006, 55(6): 1712-1724.

[14] 李林桐, 蔡育炜, 陆屹, 等. 基于蒙特卡洛树搜索的机载雷达编队策略优化[J]. 飞机设计, 2023, 43(2): 44- 48. [LI L T, CAI Y W, LU Y, et al. Optimal formation strategy for airborne radar detection based on monte Carlo tree search[J]. Aircraft Design, 2023, 43(2): 44-48.]

[15] 郭瑞玲, 苑林, 谢东明, 等. 载货汽车燃油经济性与整车质量的相关性研究[J]. 汽车工程, 2015, 37(6): 613- 616. [GUO R L, YUAN L, XIE D M, et al. A study on the correlation between the fuel economy and total mass of trucks[J]. Automotive Engineering, 2015, 37(6): 613- 616.]

露天矿作业区无人矿车协同通行决策方法研究

Collaborative Driving Decision-making Method of Unmanned Mining Trucks in Open-pit Mine Operation Areas

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	刘美岐, 金楷然, 李雅澜, 郭戈. 城市道路智能网联车辆轨迹鲁棒控制方法[J]. 交通运输系统工程与信息, 2024, 24(4): 31-40.
[2]	温惠英, 张昕怡, 黄俊达, 许鹏鹏. 考虑动态交互作用的智能车辆轨迹预测[J]. 交通运输系统工程与信息, 2024, 24(4): 60-68.
[3]	赵朔, 奇格奇, 李培豪, 关伟. 基于脑电通道注意力机制的驾驶行为识别研究[J]. 交通运输系统工程与信息, 2024, 24(4): 283-291.
[4]	马千里, 王通, 邵帅, 贾鹏. 路测环境下无人驾驶小巴复杂场景运行评价[J]. 交通运输系统工程与信息, 2024, 24(4): 300-310.
[5]	董红召, 杨嘉炜, 全程. 车联网下公交专用道复用的动态清空控制方法[J]. 交通运输系统工程与信息, 2024, 24(3): 12-20.
[6]	刘江, 张楚, 蔡伯根, 王剑, 陆德彪. 基于样本增强的列车卫星定位伪距欺骗检测方法[J]. 交通运输系统工程与信息, 2024, 24(3): 32-42.
[7]	李建市, 徐友春, 齐尧, 谢德胜, 李华. 起伏道路条件下无人车纵向速度控制方法研究[J]. 交通运输系统工程与信息, 2024, 24(3): 43-52.
[8]	张金雷, 杨健, 刘晓冰, 陈瑶, 杨立兴, 高自友. 基于计算机视觉的轨道交通站内火灾检测与定位[J]. 交通运输系统工程与信息, 2024, 24(3): 53-63.
[9]	陈喜群, 朱奕璋, 谢宁珂, 耿茂思, 吕朝锋. 基于异构多智能体自注意力网络的路网信号协调顺序优化方法[J]. 交通运输系统工程与信息, 2024, 24(3): 114-126.
[10]	辛琪, 王嘉琪, 杨文科, 徐猛, 袁伟. 信控路段混行交通生态驾驶深度强化学习模型[J]. 交通运输系统工程与信息, 2024, 24(3): 127-139.
[11]	张磊, 彭金栓, 陈晓利. 接管场景驱动下非驾驶任务对驾驶人视觉与生理特征的影响[J]. 交通运输系统工程与信息, 2024, 24(3): 290-298.
[12]	张河山, 谭鑫, 范梦伟, 潘存书, 徐进, 张羽. 无人机高空航拍视角下小尺度车辆精确检测方法[J]. 交通运输系统工程与信息, 2024, 24(3): 299-309.
[13]	魏丽英, 吴润泽. 基于鱼群效应的智能网联车队形成与演化机理研究[J]. 交通运输系统工程与信息, 2024, 24(2): 76-85.
[14]	黄玮, 李世昌, 曾海鹏. 基于非冲突合流策略的交叉口灵活信号控制方法[J]. 交通运输系统工程与信息, 2024, 24(2): 86-95.
[15]	张玺君, 聂生元, 李喆, 张红. 基于自注意力机制的深度强化学习交通信号控制[J]. 交通运输系统工程与信息, 2024, 24(2): 96-104.