Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (4): 1153-1168.doi: 10.13229/j.cnki.jdxbgxb20210038

   

Key technologies in autonomous vehicle for engineering

Xiang-jun YU1(),Yuan-hui HUAI2,Zong-wei YAO3,Zhong-chao SUN1,An YU1()   

  1. 1.School of Mechanical and Electrical Engineering，Kunming University，Kunming 650214，China
    2.Kunming Motor Vehicle Inspection and Supervision Service Center，Kunming 650200，China
    3.School of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
  • Received:2021-01-15 Online:2021-07-01 Published:2021-07-14
  • Contact: An YU E-mail:xjykmu@126.com;34018958@qq.com

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Abstract:

As the society emphasizes on the life safety of operators and the standard of machinery performance requirements for construction， engineering vehicles are developing in the direction of autonomy， efficiency and reliability. In order to realize the automatic transfer and operation of unmanned engineering vehicles， this paper systematically summarizes the relevant technologies at home and abroad， and analyzes the research progress of key technologies of unmanned engineering vehicles in detail in terms of environment perception， motion planning， engineering operation and condition monitoring， etc. It points out that the technologies of unstructured environment identification， path planning and trajectory tracking of vehicles with variable body structure and automated operation still need to be broken through， and proposes the adoption of mechanism/structure optimization design， advanced communication means， machine learning and digital twin， etc.， which is conducive to promoting the development of key technologies of unmanned engineering vehicles.

Key words: engineering vehicles, unmanned driving, environment perception, motion planning, digital twin

CLC Number: 

  • TU689

Fig.1

Framework of intelligent earthwork system"

Fig.2

Unmanned load-haul-dump proposed by Atlas"

Fig.3

Schematic diagram of four digital map modeling methods"

Fig.4

Schematic diagram of four popular path planning techniques"

Table 1

Eight path planning algorithms and their characteristics"

算法名称核心思路主要优点主要缺点
可视图法规划问题转化为图论问题概念清晰，易于实现，多用于二维地图缺乏灵活性，无法用于三维规划
栅格图法地图栅格化简单直观，可结合多类搜索算法进行规划划分粒度影响计算，无法处理动态障碍物
人工势场法构建虚拟人工势场，模拟引斥力结构简单，实时性高，计算量小障碍物分布位置影响结果
模糊逻辑法基于模糊逻辑推理模型环境未知或发生变化时，可快速准确规划出路径障碍物数目增加影响计算速度
遗传算法模拟生物遗传时的选择、交叉和变异多点搜索，理论上可以得到全局最优解运行速度满，实际操作时会产生“早熟”收敛
神经网络算法模拟神经元网络结构与特征并列性算法，可达任意精度，可实现二、三维规划只适用于环境已知且障碍物静止
蚁群算法模拟自然界蚂蚁觅食机制分布式并行计算，效率高，不会陷入局部极优值前期速度慢，参数调节依赖于经验
RRT算法随机采样的方式生长节点无需对环境建模，可解决三维及更高维度的复杂约束问题生成路径质量低，不光滑，通常远离最优路径

Table 2

Five types of trajectory tracking algorithms and their characteristics"

算法名称核心思路主要优点主要缺点
PID控制根据系统误差，利用比例、积分、微分计算控制量算法简单，鲁棒性强，可靠性高，尤其适用于确定性控制系统控制器参数整定不良，对工况适应能力差
滑模控制系统按照预定“滑动模态”的状态轨迹有目的地改动当前状态响应快速，鲁棒性强，无需系统辨识，物理实现简单状态轨迹难以严格沿滑动模态面向平衡点滑动，在平衡点附近产生抖动
模型预测控制在每一个采样瞬间求解一个有限时域开环最优控制问题，获得当前控制动作建模方便，鲁棒性强，动态控制性能好，能有效处理多变量、有约束问题难以获得设计参数与动静态特性的解析关系，难以解决不确定性系统问题
模糊控制利用模糊数学的基本思想和理论实现对系统的精确控制简化系统设计复杂性，不依赖精确数学模型，控制器不必对被控对象建立完整的数学模式完全凭经验建立模糊规则及隶属函数，精度与决策速度相矛盾，鲁棒性差
神经网络控制人工神经元与控制理论相结合，模拟人类智能融合其他控制算法解决非线性、不确定、不确知系统的控制问题学习速度慢，稳定性、收敛性差

Fig.5

Six concepts for designing unmanned engineering vehicles"

Fig.6

Schematic of multi machine tele-remote system proposed by Atlas"

1 Kim S K, Russell J S. Framework for an intelligent earthwork system: Part I. System architecture[J]. Automation in Construction, 2003, 12(1): 1-13.
2 Dobson A A, Marshall J A, Larsson J. Admittance control for robotic loading: design and experiments with a 1‐Tonne loader and a 14‐Tonne load‐haul‐dump machine[J]. Journal of Field Robotics, 2017, 34(1): 123-150.
3 Abou Merhy B, Payeur P, Petriu E M. Application of segmented 2-D probabilistic occupancy maps for robot sensing and navigation[J]. IEEE Transactions on Instrumentation and Measurement, 2008, 57(12): 2827-2837.
4 Kuipers B, Byun Y T. A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations[J]. Robotics and Autonomous Systems, 1991, 8(1/2): 47-63.
5 Ranganathan P, Hayet J B, Devy M, et al. Topological navigation and qualitative localization for indoor environment using multi-sensory perception[J]. Robotics and Autonomous Systems, 2002, 41(2/3): 137-144.
6 Cole D M, Newman P M. Using laser range data for 3D SLAM in outdoor environments[C]∥Proceedings IEEE International Conference on Robotics and Automation, Orlando, USA, 2006: 1556-1563.
7 李宏刚, 王云鹏, 廖亚萍, 等. 无人驾驶矿用运输车辆感知及控制方法[J]. 北京航空航天大学学报, 2019, 45(11): 2335-2344.
Li Hong-gang,Wang Yun-peng,Liao Ya-ping, et al. Perception and control method of driverless mining vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2335-2344.
8 欧阳毅. 基于激光雷达与视觉融合的环境感知与自主定位系统[D]. 哈尔滨:哈尔滨工业大学航天学院, 2019.
Ouyang Yi. The environment awareness and autonomous positing system based on lidar and vision[D]. Harbin: School of Astronautics,Harbin Institute of Technology, 2019.
9 李蒙. 基于小型无人机的大范围非结构化场景数字模型构建[D]. 哈尔滨:哈尔滨工业大学机电工程学院, 2017.
Li Meng. 3D digitalization of large-scale unstructured scene by unmanned helicopter[D]. Harbin:School of Electrical and Mechanical Engineering,Harbin Institute of Technology, 2017.
10 Guisado-Pintado E, Jackson D W T, Rogers D. 3D mapping efficacy of a drone and terrestrial laser scanner over a temperate beach-dune zone[J]. Geomorphology, 2019, 328: 157-172.
11 夏磐夫, 高亮. 压路机无人驾驶设计探讨[J]. 筑路机械与施工机械化, 2019, 36(6): 22-26.
Xia Pan-fu, Gao Liang. Exploring the design of unmanned road roller[J]. Road Machinery & Construction Mechanization, 2019, 36(6): 22-26.
12 Aono T, Matsuda Y, Seino K, et al. Position estimation using GPS and dead reckoning[C]∥IEEE/SICE/RSJ International Conference on Multi-sensor Fusion and Integration for Intelligent Systems, Washington DC, USA, 1996: 533-540.
13 Reid J F, Zhang Q, Noguchi N, et al. Agricultural automatic guidance research in North America[J]. Computers and Electronics in Agriculture, 2000, 25(1): 155-167.
14 Guo L S, Zhang Q, Han S. Position estimate of off-road vehicles using a low-cost GPS and IMU[C]∥American Society of Agricultural and Biological Engineers Annual Meeting, Chicago,USA, 2002: 1-7.
15 王雪莉. 基于 WIFI 通信技术的地下矿山生产调度系统研究[D]. 西安:西安建筑科技大学矿山系统工程研究所, 2010.
Wang Xue-li. Study on underground mine production scheduling system based on WIFI communication technology[D]. Xi'an:Institute of Mining Systems Engineering, Xi'an University of Construction Science and Technology, 2010.
16 郭丽梅. 基于蜂窝无线定位的交通信息采集技术研究[D]. 长沙:中南大学交通运输工程学院, 2010.
Guo Li-mei. Study on traffic information gathering based on cellular wireless location[D].Changsha:School of Transportation Engineering,Central South University, 2010.
17 韩光, 孙宁, 李晓飞, 等. 非结构环境理解综述[J]. 计算机应用研究, 2014, 31(8): 2248-2253, 2262.
Han Guang, Sun Ning, Li Xiao-fei, et al. Unstructured scene interpretation: a review[J]. Application Research of Computers, 2014, 31(8): 2248-2253, 2262.
18 李宁, 郭江华, 蓝伟. 基于多线激光雷达的非结构化道路感知技术研究[J]. 车辆与动力技术, 2017, 38(3): 8-14.
Li Ning, Guo Jiang-hua, Lan Wei. Environment perception research based on 3D-lidar in the unstructured road[J]. Vehicle & Power Technology, 2017, 38(3): 8-14.
19 王先杰, 汪选要. 基于类Haar纹理的非结构化道路消失点检测[J]. 科学技术与工程, 2019,19(16): 221-226.
Wang Xian-jie, Wang Xuan-yao. Unstructured road vanishing point detection based on Haar-like texture[J]. Science Technology and Engineering, 2019,19(16):221-226.
20 黄俊, 侯北平, 董霏, 等. 基于方向纹理的非结构化道路消失点检测研究[J]. 图学学报, 2019,40(1):131-136.
Huang Jun, Hou Bei-ping, Dong Fei, et al. Research on vanishing point detection of unstructured road based on directional texture[J]. Journal of Graphics, 2019,40(1):131-136.
21 潘奎刚, 石朝侠. 基于主方向加权投票的非结构化道路消失点检测[J]. 计算机工程, 2017, 43(12): 237-241.
Pan Kui-gang,Shi Chao-xia. Vanishing point detection of unstructured road based on dominant orientation weighted voting[J]. Computer Engineering, 2017, 43(12): 237-241.
22 凌波, 吴靖, 叶秀清, 等. 最大熵原理在非结构化道路图像识别中的应用[J]. 电路与系统学报, 2005, 10(4): 78-81, 24.
Ling Bo, Wu Jing, Ye Xiu-qing, et al. Image recognition-based maximum entropy on unstructured road [J]. Journal of Circuits and Systems, 2005, 10(4): 78-81, 24.
23 王翔, 张娟, 方志军. 基于最大熵和边缘信息的非结构化道路检测[J]. 电子科技, 2020,33(1): 23-28.
Wang Xiang, Zhang Juan, Fang Zhi-jun. Unstructured road detection based on edge information and maximum entropy segmentation[J]. Electronic Science, 2020,33(1): 23-28.
24 李小晗. 基于双目视觉的可通行区域实时检测技术研究[D]. 西安:中国科学院西安光学精密机械研究所, 2018.
Li Xiao-han. Research on real-time detection technology of passable area based on binocular stereovision[D]. Xi 'an:Xi'an Institute of Optical Precision Machinery, Chinese Academy of Sciences, 2018.
25 王小娟. 基于双目视觉的田间道路感知和路径跟踪研究[D]. 重庆:西南大学工程技术学院, 2018.
Wang Xiao-juan. Research on field road perception and path tracking based on binocular vision[D]. Chongqing: Faculty of Engineering and Technology, Southwest University, 2018.
26 蔡兵. 基于机器视觉的雾天环境下车道线识别技术研究[D]. 重庆:重庆邮电大学汽车电子与嵌入式系统研究中心, 2016.
Cai Bing. Research of lane recognition technology in fog environment based on machine vision[D]. Chongqing: Automotive Electronics and Embedded Systems Research Center, Chongqing University of Posts and Telecommunications, 2016.
27 王燕清, 陈德运, 石朝侠. 基于单目视觉的非结构化道路检测与跟踪[J]. 哈尔滨工程大学学报, 2011, 32(3): 334-339.
Wang Yan-qing, Chen De-yun, Shi Chao-xia. Unstructured road detection and tracking based on monocular vision[J]. Journal of Harbin Engineering University, 2011, 32(3): 334-339.
28 牛牧原. 基于单目视觉的非结构化道路检测研究[D]. 洛阳:河南科技大学电气工程学院, 2019.
Niu Mu-yuan. Unstructured road detection based on monocular vision[D]. Luoyang: School of Electrical Engineering, Henan University of Science and Technology, 2019.
29 修灵彦. 车队协同情境感知系统的研究与实现[D]. 北京:北京邮电大学信息与通信工程学院, 2019.
Xiu Ling-yan. Research and implementation of platoon cooperative context sensing system[D]. Beijing: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, 2019.
30 王艺, 邓佳佳, 林毅. 适用于车联网应用的移动通信网络[J]. 电信科学, 2012, 28(6): 1-6.
Wang Yi, Deng Jia-jia, Lin Yi. Mobile communication network applied to vehicle communication[J]. Telecommunications Science, 2012, 28(6): 1-6.
31 兰琛. 车联网专用短程通信和定位技术的研究与应用[D]. 杭州:浙江大学智能系统与控制研究所, 2014.
Lan Chen. Research and application of dedicated short range communication and positioning technology in internet of vehicles[D]. Hangzhou: Institute of Intelligent Systems and Control, Zhejiang University, 2014.
32 林鹏, 徐中伟, 梅萌. 基于 LTE D2D 通信技术的列车通信[J]. 城市轨道交通研究, 2018,21(12): 83-88.
Lin Peng, Xu Zhong-wei, Mei Meng. Train communication based on LTE D2D communication technology[J]. Urban Mass Transit, 2018,21(12): 83-88.
33 甯油江, 赵津, 石晴,等. 基于 ZigBee 的多车协作控制研究[J]. 现代电子技术, 2017, 40(6): 114-117, 121.
Ning You-jiang, Zhao Jin, Shi Qing, et al. Research on multi⁃vehicle cooperative control of based on ZigBee[J]. Modern Electronics Technique, 2017, 40(6): 114-117, 121.
34 Dadhich S, Bodin U, Andersson U. Key challenges in automation of earth-moving machines[J]. Automation in Construction, 2016, 68: 212-222.
35 Mckinnon C, Marshall J A. Automatic identification of large fragments in a pile of broken rock using a time-of-flight camera[J]. IEEE Transactions on Automation Science and Engineering, 2014, 11(3): 935-942.
36 Anwar H, Abbas S M, Muhammad A, et al. Volumetric estimation of contained soil using 3D sensors[C]∥Commercial Vehicle Technology Symposium, Aachen, Germany,2014:244-253.
37 Kober J, Bagnell J A, Peters J. Reinforcement learning in robotics: a survey[J]. The International Journal of Robotics Research, 2013, 32(11): 1238-1274.
38 戴光明. 避障路径规划的算法研究[D]. 武汉:华中科技大学计算机学院, 2004.
Dai Guang-ming. Research on algorithm for avoidance obstacle path planning[D]. Wuhan: School of Computer Science, Huazhong University of Science and Technology, 2004.
39 Paden B, Čáp M, Yong S Z, et al. A survey of motion planning and control techniques for self-driving urban vehicles[J]. IEEE Transactions on Intelligent Vehicles, 2016, 1(1): 33-55.
40 Li B, Shao Z. Precise trajectory optimization for articulated wheeled vehicles in cluttered environments[J]. Advances in Engineering Software, 2016, 92: 40-47.
41 Koceski S, Panov S, Koceska N, et al. A novel quad harmony search algorithm for grid-based path finding[J]. International Journal of Advanced Robotic Systems, 2014, 11(9): 168-171.
42 Khatib O. The International Journal of Robotics Research [J]. Autonomous Robot Vehicles, 1986,5(1): 90-98.
43 盛放. 履带式起重机分段路径规划算法的设计与实现[D]. 大连:大连理工大学汽车学院, 2012.
Sheng Fang. Design and implementation of segmented path planning algorithm of crawler crane[D]. Dalian:College of Automobile,Dalian University of Technology, 2012.
44 程新景. 煤矿救援机器人地图构建与路径规划研究[D]. 北京:中国矿业大学机电与信息工程学院, 2016.
Cheng Xin-jing. Research on mapping and path planning of coal mine rescue robot[D]. Beijing: School of Electrical and Mechanical Information and Engineering,China University of Mining and Technology, 2016.
45 宋琪. 基于无人越野驾驶自主导航车辆的路径规划研究[D]. 长春:吉林大学汽车工程学院, 2008.
Song Qi. The research on path planning for autonomous cross-country navigation vehicle[D]. Changchun:College of Automotive Engineering,Jilin University, 2008.
46 宋彩云. 基于模拟退火的 ALV 越野路径规划研究[D]. 长沙:国防科学技术大学机电工程与自动化学院, 2005.
Song Cai-yun. Research on cross-country path planning of ALV based on simulate anneal algorithm[D]. Changsha:School of Electrical and Mechanical Engineering and Automation,National University of Defense Technology, 2005.
47 赵芊. 基于地理信息系统的全地形车路径规划技术研究[D]. 北京:中国航天科技集团公司第一研究院, 2016.
Zhao Qian. Research on GIS-based path planning technology for all-terrain vehicles[D]. Beijing:The First Research Institute,China Aerospace Science and Technology Corporation, 2016.
48 吴天羿, 许继恒, 刘建永, 等. 多策略蚁群算法求解越野路径规划[J]. 解放军理工大学学报: 自然科学版, 2014, 15(2): 158-164.
Wu Tian-yi, Xu Ji-heng, Liu Yong-jian, et al. Multi-strategy ant colony algorithm for cross-country path planning[J]. Journal of PLA University of Science and Technology (Natural Science Edition), 2014, 15(2): 158-164.
49 李超, 于赫年, 白桦. 仓储式多AGV系统的路径规划研究及仿真[J]. 计算机工程与应用, 2020, 56(2): 233-241.
Li Chao,Yu He-nian, Bai Hua. Research and simulation on path planning of warehouse multi-AGV system[J]. Computer Engineering and Applications, 2020, 56(2): 233-241.
50 孟祥忠, 刘健, 李鹏. 多 AGV 定位和路径规划方法研究[J]. 工业仪表与自动化装置, 2019,48(5): 7-10, 29.
Meng Xiang-zhong, Liu Jian, Li Peng. Research on multi-AGV location and path planning method[J]. Industrial Instrumentation & Automation, 2019,48(5): 7-10, 29.
51 王子意. 多 AGV 系统的路径规划与调度算法的研究[D]. 北京:北京邮电大学控制科学与工程学院, 2019.
Wang Zi-yi. Research on path planning and scheduling algorithm for multiple AGV system[D]. Beijing:School of Control Science and Engineering,Beijing University of Posts and Telecommunications, 2019.
52 宋雪倩, 胡士强. 基于 Dubins 路径的 A* 算法的多无人机路径规划[J]. 电光与控制, 2018, 25(11): 25-29.
Song Xue-qian, Hu Shi-qiang. Dual-UAV path planning by Dubins-path based A* algorithm[J]. Electronics Optics & Control, 2018, 25(11): 25-29.
53 刘山, 梁文君. 多机器人协作搬运路径规划研究[J]. 计算机工程与应用, 2010, 46(32): 197-201.
Liu Shan, Liang Wen-jun. Research on multi-robots cooperation system path planning[J]. Computer Engineering and Applications, 2010, 46(32): 197-201.
54 孙吉胜. 多台履带起重机协同吊装路径规划研究[D]. 大连:大连理工大学机械工程学院, 2016.
Sun Ji-sheng. Research on path planning for multi-cranes collaborative lifting[D]. Dalian:School of Mechanical Engineering,Dalian University of Technology, 2016.
55 牛俊财, 王忠庆, 张鹏军. 基于优化型蚁群算法在多机协同作战下的路径规划[J]. 中北大学学报:自然科学版, 2019, 40(2): 137-142.
Niu Jun-cai, Wang Zhong-qing, Zhang Peng-jun. Path planning based on optimized ant colony algorithm in multi-machine cooperative operations[J]. Journal of North University of China (Natural Science Edition), 2019, 40(2): 137-142.
56 邵俊恺, 赵翾, 杨珏, 等. 无人驾驶铰接式车辆强化学习路径跟踪控制算法[J]. 农业机械学报, 2017, 48(3): 376-382.
Shao Jun-kai, Zhao Xuan, Yang Jue, et al. Reinforcement learning algorithm for path following control of articulated vehicle[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(3): 376-382.
57 Sangyam T, Laohapiengsak P, Chongcharoen W, et al. Autonomous path tracking and disturbance force rejection of UAV using fuzzy based auto-tuning PID controller[C]∥The International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Cha-am, Thailand, 2010: 528-531.
58 武星, 楼佩煌. 基于运动预测的路径跟踪最优控制研究[J]. 控制与决策, 2009(4): 565-569.
Wu Xing, Lou Pei-huang. Optimal path tracking control based on motion prediction[J]. Control and Decision, 2009(4): 565-569.
59 黄海洋, 张建, 王宇, 等. 基于多点预瞄最优控制的智能车辆路径跟踪[J]. 汽车技术, 2018,48(10): 6-9.
Huang Hai-yang, Zhang Jian, Wang Yu, et al. Path traking for intelligent vehicle based on the optimal multipoint preview control[J]. Automobile Technology, 2018,48(10): 6-9.
60 孟宇, 汪钰, 顾青, 等. 基于预见位姿信息的铰接式车辆LQR-GA路径跟踪控制[J]. 农业机械学报, 2018, 49(6): 375-384.
Meng Yu, Wang Yu, Gu Qing, et al. LQR-GA path tracking control of articulated vehicle based on predictive information[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(6): 375-384.
61 赵翾, 杨珏, 张文明, 等. 农用轮式铰接车辆滑模轨迹跟踪控制算法[J]. 农业工程学报, 2015, 31(10): 198-203.
Zhao Xuan, Yang Jue, Zhang Wen-ming, et al. Sliding mode control algorithm for path tracking of articulated dump truck[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(10): 198-203.
62 李琳辉, 李明, 郭景华, 等. 基于视觉的智能车辆模糊滑模横向控制[J]. 大连理工大学学报, 2013, 53(5): 735-741.
Li Lin-hui, Li Ming, Guo Jing-hua, et al. Fuzzy sliding mode lateral control of intelligent vehicle based on vision[J]. Journal of Dalian University of Technology, 2013, 53(5): 735-741.
63 许正荣, 王文周, 辜丽川, 等. 基于轨迹跟踪的农用履带机器人自适应滑模控制[J]. 江苏农业学报, 2018, 34(3): 711-720.
Xu Zheng-rong, Wang Wen-zhou, Gu Li-chuan, et al. Adaptive sliding mode control for agricultural tracked robot based on trajectory tracking[J]. Jiangsu Journal of Agricultural Sciences, 2018, 34(3): 711-720.
64 胡家铭, 胡宇辉, 陈慧岩, 等. 基于模型预测控制的无人驾驶履带车辆轨迹跟踪方法研究[J]. 兵工学报, 2019, 40(3): 456-463.
Hu Jia-ming, Hu Yu-hui, Chen Hui-yan, et al. Research on trajectory tracking of unmanned tracked vehicles based on model predictive control[J]. Acta Armamentarii, 2019, 40(3): 456-463.
65 孟宇, 甘鑫, 白国星. 基于预瞄距离的地下矿用铰接车路径跟踪预测控制[J]. 工程科学学报, 2019, 41(5): 662-671.
Meng Yu, Gan Xin, Bai Guo-xing. Path following control of underground mining articulated vehicle based on the preview control method[J]. Chinese Journal of Engineering, 2019, 41(5): 662-671.
66 吴俊丽. 基于单目视觉的无人驾驶汽车轨迹跟踪控制系统研究[D]. 西安:长安大学机械工程学院, 2018.
Wu Jun-li. Trajectory tracking control system of unmanned vehicle based on monocular vision[D]. Xi'an:School of Mechanical Engineering,Chang'an University, 2018.
67 侯小强. 基于感控一体化的铰接车辆动态协调控制方法[D]. 大连:大连理工大学汽车工程学院, 2018.
Hou Xiao-qiang. Perception-control integrated designing method for tractor-trailer vehicles[D]. Dalian:School of Automotive Engineering,Dalian University of Technology, 2018.
68 龚建伟, 高峻尧, 熊光明. 基于航向示教再现的履带式移动机器人路径跟踪[J]. 兵工学报, 2003, 24(1): 102-105.
Gong Jian-wei, Gao Jun-yao, Xiong Guang-ming. Heading teaching-playback based path following control for a tracked mobile robot[J]. Acta Armamentarii, 2003, 24(1): 102-105.
69 赵登峰, 王国强, 许纯新, 等. 基于模糊神经网络的智能履带车路径跟踪系统[J]. 农业工程学报, 2003, 19(2): 149-152.
Zhao Deng-feng, Wang Guo-qiang, Xu Chun-xin, et al. Path following system based on fuzzy neural networks for intelligently tracked vehicle[J]. Transactions of the Chinese Society of Agricultural Engineering, 2003, 19(2): 149-152.
70 王浩, 林棻, 张尧文. 基于模拟退火算法的无人驾驶车辆轨迹跟踪控制[J]. 重庆理工大学学报:自然科学版, 2015, 29(11): 106-111, 119.
Wang Hao, Lin Fen, Zhang Yao-wen. Research on trajectory tracking control of self-driving vehicle based on simulated annealing algorithm[J]. Journal of Chongqing University of Technology (Natural Science), 2015, 29(11): 106-111, 119.
71 Almqvist H. Automatic bucket fill[D]. Linköping:Linköping University, 2009.
72 吴传玉. 铲土运输机械铲掘阻力形成机理研究[D]. 长春:吉林大学机械科学与工程学院, 2011.
Wu Chuan-yu. Research on the formation mechanism of shover-grubbing resistance of earth-moving machinery[D]. Changchun:College of Mechanical Sciences and Engineering,Jilin University, 2011.
73 宁俏俏. 装载机铲掘作业轨迹的自适应控制仿真研究[D]. 长春:吉林大学机械科学与工程学院, 2008.
Ning Qiao-qiao. Research on adaptive control of loaders in digging[D]. Changchun:College of Mechanical Sciences and Engineering,Jilin University, 2008.
74 吉林工业大学工程机械教研室. 轮式装载机[M]. 北京:中国建筑工业出版社, 1982.
75 Danko G L, Knowles J S, Tiwari R. Digging trajectory analysis using camera vision[J]. IFAC Proceedings Volumes, 2006, 39(22): 91-96.
76 朱圣兵. 液压挖掘机器人轨迹跟踪综合控制策略方案研究[D]. 杭州:浙江大学机械工程学院, 2006.
Zhu Sheng-bing. Research on control of trajectory tracking of a robotic hydraulic excavator[D]. Hangzhou: College of Mechanical Engineering, Zhejiang University, 2006.
77 于向军, 槐元辉, 李学飞, 等. 基于克里金和粒子群算法的装载机铲掘轨迹规划[J]. 吉林大学学报:工学版, 2020, 50(2): 437-444.
Yu Xiang-jun, Huai Yuan-hui, Li Xue-fei. Shoveling trajectory planning method for wheel loader based on kriging and particle swarm optimization[J]. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(2): 437-444.
78 孙雪飞. 基于神经网络 PID 的挖掘机轨迹控制系统的实验研究[D]. 哈尔滨:哈尔滨工业大学机电工程学院, 2016.
Sun Xue-fei. Experimental research on the control system of excavator's trajectory based on neural network PID[D]. Harbin:School of Electrical and Mechanical Engineering,Harbin Institute of Technology, 2016.
79 钟荣康. 装载机自动铲装作业轨迹控制器研发[D]. 杭州:浙江大学机械工程学院, 2011.
Zhong Rong-kang. Study of auto-dig working trajectory controller of loader's work tools[D]. Hangzhou:School of Mechanical Engineering,Zhejiang University, 2011.
80 Maeda G J. Learning and reacting with inaccurate prediction: applications to autonomous excavation[D]. Sydney: University of Sydney, 2013.
81 蔡顶春, 杨士敏, 谢敏. 遥控式矿用装载机的系统设计研究[J]. 机械工程与自动化, 2013,41(4): 127-129.
Cai Ding-chun, Yang Shi-min, Xie Min. System design of remote-controlled mining loader[J]. Mechanical Engineering & Automation, 2013,41(4): 127-129.
82 万信群. 地下装载机视频遥控系统简介[J]. 有色设备, 2012,26(6):1-4.
Wan Xin-qun. Brief introduction of video remote control system of the underground loader[J]. Nonferrous Metallurgical Equipment, 2012, 26(6):1-4.
83 Sauer M, Leutert F, Schilling K. An augmented reality supported control system for remote operation and monitoring of an industrial work cell[J]. IFAC Proceedings Volumes, 2010, 43(23): 83-88.
84 Oh K W, Kim D, Kim N H, et al. The virtual environment for force-feedback experiment of excavator using a novel designed haptic device[C]∥th International Symposium on Automation and Robotics in Construction, Seoul,Korea,2011:51-56.
85 王萍. 大型电铲铲斗结构健康监测研究[D].北京:中国矿业大学机电与信息工程学院, 2018.
Wan Ping. Research on structural health monitoring of large scale electric shovel bucket[D].Beijing:School of Electrical and Mechanical and Information Engineering,China University of Mining and Technology, 2018.
86 蒋玉秀, 鄂加强, 杨黔清, 等. 基于 PLC 的矿用自卸车自动润滑注油过程控制实现[J]. 煤矿机械, 2008, 29(4): 162-164.
Jiang Yu-xiu, Jia-qiang E, Yang Qian-qing, et al. Realization on process control of self-lubrication oil injection from mineral product dump truck based on PLC[J]. Coal Mine Machinery, 2008, 29(4): 162-164.
87 杨春永, 刘会英, 刘海新, 等. 装载机轮胎打滑的原因及其应对措施[J]. 工程机械, 2008, 39(1): 23-24, 57,6.
Yang Chun-yong, Liu Hui-ying, Liu Hai-xin, et al. Causes of loader tire slippage and countermeasures[J]. Construction Machinery and Equipment, 2008, 39(1): 23-24, 57,6.
88 孔国华, 汪建利, 陈维涛, 等. 轮胎压路机主动防滑系统 (ASR) 研究[J]. 机床与液压, 2017, 45(20): 104-106, 137.
Kong Guo-hua, Wang Jian-li, Chen Wei-tao, et al. Research on pneumatic-tired roller initiative preventing slipping system (ASR)[J]. Machine Tool & Hydraulics, 2017, 45(20): 104-106, 137.
89 杨海滨. 大型履带式起重机远程状态监测系统智能终端的设计与实现[D]. 上海:上海交通大学机械与动力工程学院, 2008.
Yang Hai-bin. Design and implementation of an intelligent terminal for large crawler crane remote condition monitoring system[D]. Shanghai:Mechanical and Power Engineering,Shanghai Jiaotong University, 2008.
90 张磊庆, 罗鑫, 尹如法. 推土机燃油消耗测试方法探讨[J]. 建筑机械化, 2015,35(9): 27-29.
Zhang Lei-qing, Luo Xin, Yin Ru-fa. Discussion on bulldozer fuel consumption measurement method[J]. Construction Mechanization, 2015,35(9): 27-29.
91 杨顺, 蒋渊德, 吴坚, 等. 基于多类型传感数据的自动驾驶深度强化学习方法[J]. 吉林大学学报:工学版, 2019, 49(4): 1026-1033.
Yang Shun, Jiang Yuan-de, Wu Jian, et al. Autonomous driving policy learning based on deep reinforcement learning and multi-type sensor data[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1026-1033.
92 徐谦, 李颖, 王刚. 基于深度学习的行人和车辆检测[J]. 吉林大学学报:工学版, 2019, 49(5): 1661-1667.
Xu Qian, Li Ying, Wang Gang. Pedestrian-vehicle detection based on deep learning[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1661-1667.
93 张霖. 关于数字孪生的冷思考及其背后的建模和仿真技术[J]. 系统仿真学报, 2020, 32(4): 1-10.
Zhang Lin. Cold thinking about the digital twin and the modeling and simulation techniques behind it[J]. Journal of System Simulation, 2020, 32(4): 1-10.
94 Lundhede O. Multi machine control[C]∥The 6th International Platinum Conference, Johannesburg,South Africa, 2014:237-246.
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