拖拉机自动驾驶关键技术综述

doi:10.13733/j.jcam.issn.20955553.2022.06.017

摘要/Abstract

摘要： 具有更强自主作业能力的智能农机装备研发是当前我国农业机械研发的趋势，而智能化程度更高、作业更精准的自动驾驶拖拉机则是重中之重。分析并总结近年来自动驾驶领域的转向控制、制动控制以及决策等关键技术，探讨制约自动驾驶技术发展的主要问题，提出促进自动驾驶拖拉机发展的相关建议，通过多传感器及多控制技术融合、控制方法优化以及机器学习技术的应用等，解决拖拉机自动驾驶过程中传感器可靠性差、电液耦合转向控制困难、制动力分配控制困难及多目标多约束决策欠缺等，以期为我国自动驾驶拖拉机的研发和推广应用提供参考。自动驾驶技术有助于降低劳动力强度，提高机具作业效率，降低投入成本。因此，自动驾驶拖拉机在农业生产的“耕、种、管、收”各阶段得到广泛的应用。具有更强自主作业能力的智能农机装备研发是当前我国农业机械研发的趋势，而智能化程度更高、作业更精准的自动驾驶拖拉机则是重中之重。分析并总结近年来自动驾驶领域的关键技术，探讨制约自动驾驶技术发展的主要问题，提出促进自动驾驶拖拉机发展的相关建议，通过多传感器及多控制技术融合、控制方法优化以及机器学习技术的应用等，解决拖拉机自动驾驶过程中传感器可靠性差、电液耦合转向控制困难、制动力分配控制困难及多目标多约束决策欠缺等，以期为我国自动驾驶拖拉机的研发和推广应用提供参考。

关键词: 自动驾驶拖拉机, 转向控制, 制动控制, 智能决策

Abstract: The research and development of intelligent agricultural machinery equipment with stronger independent operation ability is the current trend of agricultural machinery research and development in China. The selfdriving tractor with higher intelligence and more accurate operation is the top priority. The article analyzes and summarizes the key technologies of steering control, braking control and decision making in the field of automatic driving in recent years, discusses the main problems that restrict the development of automatic driving technology, and puts forward relevant suggestions to promote the development of automatic driving tractors. Through the fusion of multisensor and multicontrol technologies, the optimization of control methods and the application of machine learning technology, the poor reliability of sensors, the difficulty of electrohydraulic coupling steering control, the difficulty of braking force distribution control and the lack of multiobjective multiconstraint decision making in the automatic driving process of tractors are solved, with a view to providing reference for the development and promotion of automatic driving tractors in China.

Key words: automatic driving tractor, steering control, brake control, intelligent decision

中图分类号:

S219

徐广飞, 陈美舟, 金诚谦, 苗河泉, 逄焕晓, 刁培松. 拖拉机自动驾驶关键技术综述[J]. 中国农机化学报, 2022, 43(6): 126-134.

Xu Guangfei, Chen Meizhou, Jin Chengqian, Miao Hequan, Pang Huanxiao, Diao Peisong. . A review of key technology of tractor automatic driving[J]. Journal of Chinese Agricultural Mechanization, 2022, 43(6): 126-134.

参考文献

［1］韩树丰, 何勇, 方慧. 农机自动导航及无人驾驶车辆的发展综述（英文）［J］. 浙江大学学报（农业与生命科学版）, 2018, 44（4）: 381-391, 515.
Han Shufeng, He Yong, Fang Hui. Recent development in automatic guidance and autonomous vehicle for agriculture: A review ［J］. Journal of Zhejiang University （Agriculture & Life Sciences）, 2018, 44（4）: 381-391, 515.
［2］罗锡文, 张智刚, 赵祚喜, 等. 东方红X-804拖拉机的DGPS自动导航控制系统［J］. 农业工程学报, 2009, 25（11）: 139-145.
Luo Xiwen, Zhang Zhigang, Zhao Zuoxi, et al. Design of DGPS navigation control system for Dongfanghong X-804 tractor ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25（11）: 139-145.
［3］黎永键, 赵祚喜, 黄培奎, 等. 基于DGPS与双闭环控制的拖拉机自动导航系统［J］. 农业机械学报, 2017, 48（2）: 11-19.
Li Yongjian, Zhao Zuoxi, Huang Peikui, et al. Automatic navigation system of tractor based on DGPS and double closedloop steering control ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48（2）: 11-19.
［4］王诗冬. 基于北斗卫星导航的拖拉机辅助驾驶系统研究［D］. 镇江: 江苏大学, 2017.
［5］张京, 陈度, 王书茂, 等. 农机INS/GNSS组合导航系统航向信息融合方法［J］. 农业机械学报, 2015, 46（S1）: 1-7.
Zhang Jing, Chen Du, Wang Shumao, et al. Research of INS/GNSS heading information fusion method for agricultural machinery automatic navigation system ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46（S1）: 1-7.
［6］刘进一. 基于速度自适应的拖拉机自动导航控制系统研究［D］. 北京: 中国农业大学, 2017.
［7］张硕, 刘进一, 杜岳峰, 等. 基于速度自适应的拖拉机自动导航控制方法［J］. 农业工程学报, 2017, 33（23）: 48-55.
Zhang Shuo, Liu Jinyi, Du Yuefeng, et al. Method on automatic navigation control of tractor based on speed adaptation ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33（23）: 48-55.
［8］刘军, 袁俊, 蔡骏宇, 等. 基于GPS/INS和线控转向的农业机械自动驾驶系统［J］. 农业工程学报, 2016, 32（1）: 46-53.
Liu Jun, Yuan Jun, Cai Junyu, et al. Autopilot system of agricultural vehicles based on GPS/INS and steerbywire ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32（1）: 46-53.
［9］刘沛, 陈军, 张明颖. 基于激光导航的果园拖拉机自动控制系统［J］. 农业工程学报, 2011, 27（3）: 196-199.
Liu Pei, Chen Jun, Zhang Mingying. Automatic control system of orchard tractor based on laser navigation ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27（3）: 196-199.
［10］孟庆宽. 基于机器视觉的农业车辆—农具组合导航系统路径巧别及控制方法研究［D］. 北京: 中国农业大学, 2014.
［11］翟志强. 基于虚拟现实的拖拉机双目视觉导航试验方法研究［D］. 北京: 中国农业大学, 2017.
［12］ Du Y, Schiller B, Krantz D, et al. ALX: Autonomous vehicle guidance for roadway following and obstacle avoidance ［C］. IEEE International Conference on Systems, 1995.
［13］ Blackmore B S, Griepentrog H W, Nielsen H, et al. Development of a deterministic autonomous tractor ［C］. CIGR International Conference, 2004.
［14］ Kayacan E, Kayacan E, Ramon H, et al. Learning in centralized nonlinear model predictive control: Application to an autonomous tractortrailer system ［J］. IEEE Transactions on Control Systems Technology, 2014, 23（1）: 197-205.
［15］ Thanpattranon P, Ahamed T, Takigawa T. Navigation of autonomous tractor for orchards and plantations using a laser range finder: Automatic control of trailer position with tractor ［J］. Biosystems Engineering, 2016, 147: 90-103.
［16］陈子义. 挪草机器人电液伺服控制及作物定位信息优化方法研究［D］. 北京: 中国农业大学, 2016.
［17］ Lee C J, Jeon C W, Han Xiongzhe, et al. Electrohydraulic steering control system for automated steering of an agricultural tractor ［J］. An ASABE Meeting Presentation, 2017.
［18］ Chang Joo Lee. Application of electrohydraulic proportional valve for steering improvement of an autonomous tractor ［D］. Seoul: Seoul National University, 2017.
［19］房素素, 鲁植雄, 王增才, 等. 拖拉机线控液压转向系统设计及样车性能试验［J］. 农业工程学报, 2017, 33（10）: 86-93.
Fang Susu, Lu Zhixiong, Wang Zengcai, et al. Design and prototype performance experiments of steeringbywire hydraulic pressure system of tractor ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33（10）: 86-93.
［20］ Wu D, Zhang Q, Reid J F, et al. Adaptive control of electrohydraulic steering system for wheeltype agricultural tractors ［J］. An ASAE Meeting Presentation, Toronto, 1999.
［21］ Hu Shupeng, Fu Weiqiang, Li You, et al. Research on automatic steering control system of full hydraulic steering tractor ［C］. International Conference on Computer and Computing Technologies in Agriculture. Springer, Cham, 2017.
［22］王庆. 拖拉机电控液压动力转向系统的转向机构及液压系统设计［D］. 南京: 南京农业大学, 2010.
［23］耿国庆. 大客车新型电控液压转向系统控制方法与关键技术研究［D］. 镇江: 江苏大学, 2014.
［24］徐广飞, 逄焕晓, 陈美舟, 等. 拖拉机电液耦合转向试验平台设计与硬件在环试验［J］. 农业机械学报, 2020, 51（S1）: 525-534, 549.
Xu Guangfei, Pang Huanxiao, Chen Meizhou, et al. Design of hardware in loop tractor electrohydraulic coupling steering test platform ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51（S1）: 525-534, 549.
［25］陈志刚. 电动液压转向助力系统仿真试验平台研究［D］. 长沙: 湖南大学, 2013.
［26］禤文伟. 大中型商用车蓄能式电动液压助力转向系统开发［D］. 北京: 清华大学, 2015.
［27］ Han Yaning, Zheng Hongyu, Wan Ying, et al. Assistance characteristics and control strategy of electrohydraulic power steering systems on commercial vehicles ［J］. SAE Technical Paper 2015-01-2723, 2015.
［28］吕程盛. 商用车电液耦合转向系统控制策略研究［D］. 吉林: 吉林大学, 2017.
［29］ Zhou Xiaochuan, Zhao Wanzhong, Wang Chunyan. Performance analysis and multiobjective optimization design of vehicle electrohydraulic compound steering system ［J］. DEStech Transactions on Environment Energy and Earth Science, 2019.
［30］ Zhao Wanzhong, Zhou Xiaochuan, Wang Chunyan, et al. Energy analysis and optimization design of vehicle electrohydraulic compound steering system ［J］. Applied Energy, 2019, 255: 1-17.
［31］ Kralev J, Mitov A, Slavov T, et al. Optimal threeloop cascade PIPPI controller for electrohydraulic power steering system ［J］. IOP Conference Series Materials Science and Engineering, 2019, 664: 012011.
［32］ Wang Yunchao, Wang Chengzhi. Matching and optimising analysis of multiaxle steering vehicle steering system ［J］. International Journal of Vehicle Design, 2018, 76: 82-107.
［33］ Muro T. Trafficability control system for a tractor traveling up and down a weak slope terrain using initial track belt tension ［J］. Soils and foundations, 1995, 35（1）: 55-64.
［34］ Nishiike Y, Umeda M, Fujii M. Braking force distribution control for an agricultural tractor ［C］. Collection of Extent Abstracts of 2004 CIGR International Conference （Volume. 2）. 2004.
［35］董金松. 半挂汽车列车弯道制动行驶方向稳定性及协调控制策略研究［D］. 吉林: 吉林大学, 2010.
［36］ Zong Changfu, Zhu Tianjun, Wang Chang, et al. Multiobjective stability control algorithm of heavy tractor semi-trailer based on differential braking ［J］. Chinese Journal of Mechanical Engineering, 2012, 25（1）: 88-97.
［37］ Goodarzi A, Behmadi M, Esmailzadeh E. Optimized braking force distribution during a brakinginturn maneuver for articulated vehicles ［C］. 2010 International Conference on Mechanical and Electrical Technology. IEEE, 2010: 555-559.
［38］ Henderson L, Cebon D. Fullscale testing of a novel slip control braking system for heavy vehicles ［J］. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2016, 230（9）: 1221-1238.
［39］ Zhu B, Feng Y, Zhao J. Modelbased pneumatic braking force control for the emergency braking system of tractorsemitrailer ［R］. SAE Technical Paper, 2018.
［40］ Zheng H, Hu J, Ma S. Research on simulation and control of differential braking stability of tractor semitrailer ［R］. SAE Technical Paper, 2015.
［41］ Zheng H, Liu C, Wang L. A braking force distribution strategy in integrated braking system based on wear control and hitch force control ［R］. SAE Technical Paper, 2018.
［42］ Samorodov V, Kozhushko A, Pelipenko E. Influence of change of hydraulic machine control parameter during braking of the tractor with the continuously variable transmission ［J］. Technology audit and production reserves, 2017, 41（36）: 11-18.
［43］ Yao Nianmeng, Lu Yufeng, Zhu Teng, et al. A study on regenerative braking of tractorsemitrailer combination based on AMESim ［J］. Automotive Engineering, 2017, 39（5）: 530-534.
［44］ Jardine P T. A reinforcement learning approach to predictive control design: autonomous vehicle applications ［D］. Queens University （Canada）, 2018.
［45］秦政. 基于自主和自学习行为智能体的AUV运动规划研究［D］. 吉林: 哈尔滨工程大学, 2008.
［46］江浩斌, 施凯津, 华一丁, 等. 基于hp自适应伪谱法的智能汽车紧急变道轨迹规划与优化［J］. 中国公路学报, 2019, 32（6）: 71-78.
Jiang Haobin, Shi Kaijin, Hua Yiding, et al. Lanechanging trajectory planning and optimization for intelligent vehicle through hpadaptive pseudospectral method ［J］. China Journal of Highway and Transport, 2019, 32（6）: 71-78.
［47］ Bhopale P, Kazi F, Singh N. Reinforcement learning based obstacle avoidance for autonomous underwater vehicle ［J］. Journal of Marine Science and Application, 2019, 18（2）: 228-238.
［48］ Wang P, Chan C Y, de La Fortelle A. A reinforcement learning based approach for automated lane change maneuvers ［C］. 2018 IEEE Intelligent Vehicles Symposium （IV）. IEEE, 2018: 1379-1384.
［49］ Ji X, He X, Lü C, et al. A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation ［J］. Vehicle System Dynamics, 2018, 56（6）: 923-946.
［50］ Lü C, Xing Y, Lu C, et al. Hybridlearningbased classification and quantitative inference of driver braking intensity of an electrified vehicle ［J］. IEEE Transactions on Vehicular Technology, 2018, 67（7）: 5718-5729.
［51］ He X, Ji X, Yang K, et al. Autonomous emergency braking based on radial basis function neural network variable structure control for collision avoidance ［C］. 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference （ITNEC）. IEEE, 2017: 378-383.
［52］ Ji X, He X, Lü C, et al. Adaptiveneuralnetworkbased robust lateral motion control for autonomous vehicle at driving limits ［J］. Control Engineering Practice, 2018, 76: 41-53.
［53］ Bhattacharyya R P, Phillips D J, Wulfe B, et al. Multiagent imitation learning for driving simulation ［C］. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS）. IEEE, 2018: 1534-1539.
［54］ Chen Y. Learningbased lane following and changing behaviors for autonomous vehicle ［D］. Masterst thesis, Carnegie Mellon University, Pittsburgh, PA, 2018.
［55］ Altché F, de La Fortelle A. An LSTM network for highway trajectory prediction ［C］. 2017 IEEE 20th International Conference on Intelligent Transportation Systems （ITSC）. IEEE, 2017: 353-359.
［56］季学武, 费聪, 何祥坤, 等. 基于LSTM网络的驾驶意图识别及车辆轨迹预测［J］. 中国公路学报, 2019, 32（6）: 34-42.
Ji Xuewu, Fei Cong, He Xiangkun, et al. Intention recognition and trajectory prediction for vehicles using LSTM network ［J］. China Journal of Highway and Transport, 2019, 32（6）: 34-42.
［57］ Buechel M, Knoll A. Deep reinforcement learning for predictive longitudinal control of automated vehicles ［C］. 2018 21st International Conference on Intelligent Transportation Systems （ITSC）. IEEE, 2018: 2391-2397.
［58］韩向敏, 鲍泓, 梁军, 等. 一种基于深度强化学习的自适应巡航控制算法［J］. 计算机工程, 2018, 44（7）: 32-35, 41.
Han Xiangmin, Bao Hong, Liang Jun, et al. An adaptive cruise control algorithm based on deep reinforcement learning ［J］. Computer Engineering, 2018, 44（7）: 32-35, 41.
［59］ Yu L, Shao X, Wei Y, et al. Intelligent landvehicle model transfer trajectory planning method based on deep reinforcement learning ［J］. Sensors, 2018, 18（9）: 2905.
［60］ Yu A, PalefskySmith R, Bedi R. Deep reinforcement learning for simulated autonomous vehicle control ［J］. Course Project Reports: Winter, 2016.

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