Scientific and Technical Issues in Cross-terrain Process and Driving Device Design

doi:10.12382/bgxb.2024.0977

Abstract

Abstract:

Cross-terrain scenarios are common in human activities and production. The ongoing development of cross-terrain equipment characterized by high mobility, low power consumption, and safety is crucial. Such equipment enables human to access inaccessible areas, particularly in emergency rescue and military operations. This paper aims to establish a new technical scientific discipline. It introduces cross-terrain science and defines it. The paper methodically addresses major scientific and technological challenges in four areas of cross-terrain science: surface soil mechanics and adhesion, driving device design theory and method, cross-terrain driving dynamics and control, and ergonomics and biomimetics of cross-terrain machines. Additionally, it recommends priorities for future research and development in cross-terrain technologies and equipment.

Key words: cross-terrain science, high mobility, driving device, transport equipment, robot

CLC Number:

TJ810.1

MAO Ming, CHEN Yijie. Scientific and Technical Issues in Cross-terrain Process and Driving Device Design[J]. Acta Armamentarii, 2024, 45(12): 4191-4204.

Figures/Tables 8

Fig.1 Vehicle-soil interaction model[8]

Fig.2 Ripsaw MS1 unmanned vehicle[24]

Fig.3 Handle wheel-legged robot[25]

Fig.4 Multi-mode extreme travel suspension

Fig.5 Genshock energy feedback damper[35]

Fig.6 Reconfigurable wheeled track[30]

Fig.7 NATO NG-NRMM[44]

Fig.8 Zhu Rong Mars rover[73]

References 98

[1]	中华人民共和国国务院新闻办公室. 国防白皮书: 中国的军事战略[R/OL]. (2015-05-26). https://www.gov.cn/zhengce/2015-05/26/content_2868988.htm.
	Information Office of the State Council of the People’s Republic of China. Whitepaper on national defense: China’s military strategy[R/OL]. (2015-05-26). https://www.gov.cn/zhengce/2015-05/26/content_2868988.htm. (in Chinese)
[2]	BEKKER M G. Theory of land locomotion[M]. Ann Arbor, MI, US: The University of Michigan Press, 1956.
[3]	JENKINS S A. Clandestine methods for the determination of beach trafficability: SIO-REF-85-27[R]. Santiago, CA, US: Scripps Institution of Oceanography, 1985.
[4]	ROWLAND D. Tracked vehicle ground pressure and its effect on soft ground performance[C]// Proceedings of the Fourth International Conference on Terrain-Vehicle Systems. Stockholm, Sweden: ISTVS, 1972: 353-384.
[5]	JANOSI Z. Analysis and presentation of soil-vehicle mechanics data[J]. Journal of Terramechanics, 1965, 2(3): 69-79.
[6]	WONG J Y, REECE A R. Prediction of rigid wheel performance based on the analysis of soil-wheel stresses Part I. performance of driven rigid wheels[J]. Journal of Terramechanics, 1967, 4(1): 81-98.
[7]	YAMASHITA H, JAYAKUMAR P, ALSALEH M, et al. Physics-based deformable tire-soil interaction model for off-road mobility simulation and experimental validation[J]. Journal of Computational and Nonlinear Dynamics, 2018, 13(2): 021002.
[8]	WASFY T, JAYAKUMAR P. Next generation NATO reference mobility model complex terramechanics. Part 1: definition and literature review[J]. Journal of Terramechanics, 2021, 96: 45-57.
[9]	BORAZJANI A, HOWARD I, GRIFFING Z, et al. Analyzing mobility trends of tracked vehicles on fine-grained soils in a progressive slip condition using full-scale test data presented in DROVE 3.0[J]. Journal of Terramechanics, 2023, 106: 21-34.
[10]	EDWARDS B. Co-simulation of MBD models with dem code to predict mobility on soft soil[C]// Proceedings of the 2018 Ground Vehicle Systems Engineering and Technology Symposium. Novi, MI, US: NDIA, 2018.
[11]	陈秉聪. 土壤-车辆系统力学[M]. 北京: 中国农业出版社, 1981.
	CHEN B C. Mechanics of soil-vehicle system[M]. Beijing: China Agriculture Press, 1981. (in Chinese)
[12]	培克 M G. 陆用车辆行驶原理[M]. 孙凯南, 译. 北京: 机械工业出版社, 1962.
	BEKKER M G. Theory of land locomotion[M]. SUN K N, translated. Beijing: China Machine Press, 1962. (in Chinese)
[13]	李杰, 庄继德, 翟忠魁. 车辆行驶的表层沙土非线性弹性本构模型的试验研究[J]. 农业工程学报, 1996(2): 54-58.
	LI J, ZHUANG J D, ZHAI Z K. Test and study on the nonlinear elastic constitutive model of top sand under moving vehicle[J]. Transactions of the Chinese Society of Agricultural Engineering, 1996(2): 54-58. (in Chinese)
[14]	杨聪彬, 董明明, 顾亮, 等. 考虑履刺形状的履带板土壤推力研究[J]. 北京理工大学学报, 2015, 35(11): 1118-1121.
	YANG C B, DONG M M, GU L, et al. Research on soil shear strength considering the shape of grouser[J]. Transactions of Beijing Institute of Technology, 2015, 35(11): 1118-1121. (in Chinese)
[15]	刘聚德. 车辆沙地行驶理论[M]. 北京: 机械工业出版社, 1996.
	LIU J D. Theory of vehicle driving in sand[M]. Beijing: China Machine Press, 1996. (in Chinese)
[16]	张克健. 车辆地面力学[M]. 北京: 国防工业出版社, 2002.
	ZHANG K J. Vehicle ground mechanics[M]. Beijing: National Defense Industry Press, 2002. (in Chinese)
[17]	王玉杰. 基于离散元法的车轮与松软地面相互作用分析[D]. 长春: 吉林大学, 2012.
	WANG Y J. The Analysis of the interaction between wheeland soft ground based on DEM[D]. Changchun: Jilin University, 2012. (in Chinese)
[18]	杨涛. 基于离散元法的弹性车轮与松软地面相互作用分析[D]. 长春: 吉林大学, 2021.
	YANG T. Analysis of interaction between elastic wheel and soft ground based on discrete element method[D]. Changchun: Jilin University, 2021. (in Chinese)
[19]	肖万港, 周云波, 傅耀宇, 等. 土壤对军用越野车辆机动性能影响分析[J]. 兵工学报, 2024, 45(1): 288-298. doi: 10.12382/bgxb.2022.0528
	XIAO W G, ZHOU Y B, FU Y Y, et al. Analysis of the influence of soil on the maneuverability of military off-road vehicles[J]. Acta Armamentarii, 2024, 45(1): 288-298. (in Chinese) doi: 10.12382/bgxb.2022.0528
[20]	黄雪涛, 谢虎, 李加坤, 等. 履带式农业装备黏土壤土通过性研究[J]. 中国农机化学报, 2023, 44(6): 114-119.
	HUANG X T, XIE H, LI J K, et al. Study on clay loam transmissibility of crawler agricultural equipment[J]. Journal of Chinese Agricultural Mechanization, 2023, 44(6): 114-119. (in Chinese) doi: 10.13733/j.jcam.issn.20955553.2023.06.016
[21]	黄雪涛, 李玉琼, 董明明, 等. 履带装备超湿黏土壤土地面通过性研究[J]. 光学精密工程, 2023, 31(5): 719-728.
	HUANG X T, LI Y Q, DONG M M, et al. Study on super-wetting clay soil trafficability of track equipment[J]. Optics and Precision Engineering, 2023, 31(5): 719-728. (in Chinese)
[22]	刘卫鹏. 高速履带车辆与软质地面动态耦合机理研究[D]. 北京: 北京理工大学, 2024.
	LIU W P. Research on the dynamic coupling mechanism between high-speed tracked vehicles and soft ground[D]. Beijing: Beijing Institude of Technology, 2024. (in Chinese)
[23]	孔涵. 考虑轮壤耦合作用的三轴分布式驱动车辆转矩分配[D]. 北京: 北京理工大学, 2024.
	KONG H. Torque distribution of a three-axis distributed drive vehicle considering wheel-soil coupling[D]. Beijing: Beijing Institude of Technology, 2024. (in Chinese)
[24]	李晓东. 世界上最快的无人战车——美国“粗齿锯”高速履带车[J]. 轻兵器, 2010(4): 20-23.
	LI X D. The world’s fastest unmanned combat vehicle-the American Ripsaw MS1 high-speed tracked vehicle[J]. Small Arms, 2010(4): 20-23. (in Chinese)
[25]	Stuffi. Le nouveau robot de Boston Dynamics estterrifi-ant[EB/OL]. (2017-02-28). in French)
[26]	CARLOS L, FRANCISCO R, ZENG S W. Multi-objective optimization framework for designing a vehicle suspension system: a comparison of optimization algorithms[J]. Advances in Engineering Software, 2023, 176: 103375.
[27]	MOHAMED I, ANAS S. Passive vehicle suspension system optimization using Harris Hawk Optimization algorithm[J]. Mathematics and Computers in Simulation, 2022, 191: 328-345.
[28]	SMITH M C, WANG F C. Performance benefits in passive vehicle suspensions employing inerters[J]. Vehicle System Dynamics, 2004, 42(4) : 235-257.
[29]	杜甫, 毛明, 陈轶杰, 等. 基于动力学模型与参数优化的ISD悬挂结构设计及性能分析[J]. 振动与冲击, 2014, 33(6): 59-65.
	DU F, MAO M, CHEN Y J, et al. Structure design and performance analysis of inerter-spring-damper suspension structure based on dynamic model and parameter optimization[J]. Journal of Vibration and Shock, 2014, 33(6): 59-65. (in Chinese)
[30]	LOZ B. DARPA demonstrates 6 new technologies behind the agile combat vehicles of tomorrow[EB/OL]. (2018-06-25). https://newatlas.com/darpa-gxv-t-demonstration-military-vehicle-technology/55198.
[31]	WONG P K, LI W F, MA X B, et al. Adaptive event-triggered dynamic output feedback control for nonlinear active suspension systems based on interval type-2 fuzzy method[J]. Mechanical Systems and Signal Processing, 2024, 212: 111280.
[32]	LI Z H, CHEN H T, WU W T, et al. Dynamic output feedback Fault-Tolerant control for switched vehicle active suspension delayed systems[J]. IEEE Transactions on Vehicular Technology, 2024, 73(8): 11059-11071.
[33]	KIM J, LEE T, KIM C J, et al. Development of data-based model predictive control for continuous damping and air spring suspension system[J]. Control Engineering Practice, 2024, 142, 10577.
[34]	JONES D W. Easy Ride: Bose Corp. uses speaker technology to give cars adaptive suspension[J]. IEEE Spectrum, 2005, 42(5): 12-14.
[35]	ZHANG P, WANG R C. A comprehensive review on regenerative shock absorber systems[J]. Journal of Vibration Engineering & Technologies, 2019, 8: 225-246.
[36]	祝恒佳, 田思远, 李双宝, 等. 车辆电磁馈能式动力吸振器设计[J]. 浙江大学学报(工学版), 2023, 57(8): 1644-1654.
	ZHU H J, TIAN S Y, LI S B, et al. Design of vehicle electromagnetic energy feedback power absorber[J]. Journal of Zhejiang University (Engineering Edition), 2023, 57(8):1644-1654. (in Chinese)
[37]	朱惠莲. 松软地面行走机构的研究现状和展望[J]. 科技信息, 2006(8): 138-139.
	ZHU H L. Research status and prospects of walking mechanisms on soft ground[J]. Science & Technology Information, 2006(8): 138-139. (in Chinese)
[38]	司跃元, 赵新华, 侍才洪, 等. 轮履复合机器人行走机构的设计及运动学分析[J]. 机械设计与制造, 2013(7): 191-193.
	SI Y Y, ZHAO X H, SHI C H, et al. Walking mechanism design and kinematic analysis of wheel-tracked composite robot[J]. Machinery Design & Manufacture, 2013 (7): 191-193. (in Chinese)
[39]	OPEYEMI O, CHRISTINA M, JULIA A, et al. Omnidirectional all-terrain screw-driven robot design, modeling, and application in humanitarian demining^*[J]. IFAC-Papers-Online, 2022, 55(27): 7-12.
[40]	YAO S, CHENG X, LIU Z N, et al. An omnidirectional screw-driven forestry robot[J/OL]. Journal of Field Robotics, 2024. (2024-08-07). https://doi.org/10.1002/rob.22408.
[41]	PETRIŞOR S M, SIMION M, BÂRSAN G, et al. Humanitarian demining serial-tracked robot: design and dynamic modeling[J]. Machines, 2023, 11(5): 548.
[42]	KYLE M. DARPA invents wheels that instantly morph into triangular tank tracks[EB/OL]. (2018-06-25). https://www.popularmechanics.com/military/a21932118/darpa-wheels-become-tank-tracks.
[43]	周忠胜. 车辆装备“越野平均速度”类指标研究综述[J]. 汽车科技, 2024(3): 15-24, 30.
	ZHOU Z S. A review of research on “average off-road speed” indicators for vehicle equipment[J]. Automotive Technology, 2024 (3): 15-24, 30. (in Chinese)
[44]	MCCULLOUGH M, JAYAKUMAR P, DASCH J, et al. The next generation NATO reference mobility model development[J]. Journal of Terramechanics, 2017, 73: 49-60.
[45]	ROY S S, PRATIHAR D K. Dynamic modeling, stability and energy consumption analysis of a realistic six-legged walking robot[J]. Robotics and Computer-Integrated Manufacturing, 2013, 29(2): 400-416.
[46]	DHAOUADI R, HATAB A. Dynamic modelling of differential-drive mobile robots using Lagrange and Newton-Euler methodologies: a unified framework[J]. Advances in Robotics & Automation, 2013, 2(2): 1000107.
[47]	曲梦可, 王洪波, 荣誉. 轮腿混合机器人机械腿动力学建模与驱动预估[J]. 兵工学报, 2017, 38(8): 1619-1629. doi: 10.3969/j.issn.1000-1093.2017.08.021
	QU M K, WANG H B, RONG Y. Dynamic modeling and driving parameter prediction of mechanical leg of wheel-leg hybrid robot[J]. Acta Armamentarii, 2017, 38(8): 1619-1629. (in Chinese)
[48]	WEI W P, YIN G D, HE T Y, et al. Physics-informed data-based LPV modeling and validations of lateral vehicle dynamics[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(1): 2459-2468.
[49]	陈渐伟, 于传强, 刘志浩, 等. 多轴特种车辆的数据建模方法及横向动力学应用[J]. 兵工学报, 2023, 44(1): 165-175. doi: 10.12382/bgxb.2022.0811
	CHEN J W, YU C Q, LIU Z H, et al. Data modeling of multi-axle special vehicles and lateral dynamics applications[J]. Acta Armamentarii, 2023, 44(1): 165-175. (in Chinese) doi: 10.12382/bgxb.2022.0811
[50]	DA L M, BORTOLUZZI D, PAPINI G P R. Modelling longitudinal vehicle dynamics with neural networks[J]. Vehicle System Dynamics, 2019, 58(11): 1675-1693.
[51]	PAN Y J, NIE X B, LI Z X, et al. Data-driven vehicle modeling of longitudinal dynamics based on a multibody model and deep neural networks[J]. Measurement, 2021, 180: 109541.
[52]	张昊, 魏超, 胡纪滨, 等. 基于转向模式切换的三轴独立转向车辆路径跟踪控制研究[J]. 机械工程学报, 2024, 60(2): 243-251.
	ZHAO H, WEI C, HU J B, et al. Research on three-axis independent steering vehicle path tracking control based on steering mode switching[J]. Journal of Mechanical Engineering, 2024, 60(2): 243-251. (in Chinese)
[53]	MIN C, PAN Y J, DAI W, et al. Trajectory optimization of an electric vehicle with minimum energy consumption using inverse dynamics model and servo constraints[J]. Mechanism and Machine Theory, 2023, 181: 105185.
[54]	LU S Y, XU X J, WANG W H. Coupling dynamic model of vehicle-wheel-ground for all-terrain distributed driving unmanned ground vehicle[J]. Simulation modelling practice and theory, 2023, 128: 102817.
[55]	ALEXA O, CIOBOTARU T, GRIGORE L Ş, et al. A review of mathematical models used to estimate wheeled and tracked unmanned ground vehicle kinematics and dynamics[J]. Mathematics, 2023, 11(17): 3735.
[56]	赵轩, 王姝, 马建, 等. 分布式驱动电动汽车底盘集成控制技术综述[J]. 中国公路学报, 2023, 36(4): 221-248. doi: 10.19721/j.cnki.1001-7372.2023.04.018
	ZHAO X, WANG S, MA J. Review of chassis integrated control technology for distributed drive electric vehicles[J]. China Journal of Highway and Transport, 2023, 36(4): 221-248. (in Chinese) doi: 10.19721/j.cnki.1001-7372.2023.04.018
[57]	张雷, 徐同良, 李嗣阳, 等. 全线控分布式驱动电动汽车底盘协同控制研究综述[J]. 机械工程学报, 2023, 59(20): 261-280. doi: 10.3901/JME.2023.20.261
	ZHANG L, XU T L, LI S Y, et al. Overview on chassis coordinated control for full X-by-wire distributed drive electric vehicles[J]. Journal of Mechanical Engineering, 2023, 59(20): 261-280. (in Chinese) doi: 10.3901/JME.2023.20.261
[58]	ZHA Y F, DENG J X, QIU Y Y, et al. A survey of intelligent driving vehicle trajectory tracking based on vehicle dynamics[J]. SAE International journal of vehicle dynamics, stability, and NVH, 2023, 7(2): 221-248.
[59]	HEWING L, KABZAN J, ZEILINGER M N. Cautious model predictive control using gaussian process regression[J]. IEEE Transactions on Control Systems Technology, 2020, 28(6): 2736-2743.
[60]	ANDO N, FUJIMOTO H. Yaw-rate control for electric vehicle with active front/rear steering and driving/braking force distribution of rear wheels[C]// Proceedings of the 2010 11th IEEE International Workshop on Advanced Motion Control (AMC). Nagaoka, Japan: IEEE, 2010: 726-731.
[61]	BENEDICT J, PETER N, REINER K, et al. Torque-vectoring stability control of a four-wheel drive electric vehicle[C]// Proceedings of 2015 IEEE Intelligent Vehicles Symposium (IV). Seoul, Korea: IEEE, 2015: 1018-1023.
[62]	SUBOSITS J, GERDES J C. A synthetic input approach to slip angle based steering control for autonomous vehicles[C]// Proceedings of 2017 American Control Conference (ACC). Seattle, WA, US: IEEE, 2017: 2297-2302.
[63]	ISHIGAMI G, KEWLANI G, IAGNEMMA K. Predictable mobility a statistical approach for planetary surface exploration rovers in uncertain terrain[J]. IEEE Robotics and Automation Magazine, 2009, 16(4): 61-70.
[64]	JOSHI K, HSEIH C, MITRA S, et al. Estimating unc-ertainty of autonomous vehicle systems with generalize-d polynomial chaos: arXiv:2208.02232[R/OL]. Ithaca, NY, US: Cornell University, 2022. (2022-08-03). https://arxiv.org/abs/2208.02232.
[65]	KABZAN J, HEWING L, LINIGER A, et al. Learning-based model predictive control for autonomous racing[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3363-3370. doi: 10.1109/LRA.2019.2926677
[66]	GE Z K, MAN Z H, WANG Z, et al. Robust adaptive sliding mode control for path tracking of unmanned agricultural vehicles[J]. Computers and electrical engineering, 2023, 108: 108693.
[67]	SONG J R, TAO G, ZANG Z, et al. Isolating trajectory tracking from motion control: a model predictive control and robust control framework for unmanned ground vehicles[J]. IEEE Robotics and Automation Letters, 2023, 8(3): 1699-1706.
[68]	ZHANG B W, HUANG J, SU Y Z, et al. Distributed collaborative control of multi-vehicle autonomous cooperative transportation systems: a hierarchical constraint-following approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 25(5): 4251-4264.
[69]	机器人分类:GB/T 39405—2020[S]. 北京: 中国标准出版社, 2020.
	Classification of robot: GB/T 39405—2020[S]. Beijing: Standards Press of China, 2020. (in Chinese)
[70]	邓宗全, 范雪兵, 高海波, 等. 载人月球车移动系统综述及关键技术分析[J]. 宇航学报, 2012, 33(6): 675-689.
	DENG Z Z, FAN X B, GAO H B, et al. Review and key techniques for locomotive system of manned lunar rovers[J]. Journal of Astronautics, 2012, 33(6): 675-689. (in Chinese)
[71]	宁波. “玉兔号”是怎样被设计出来——我国首个月球车诞生记[EB/OL]. (2013-12-16). https://www.chinadaily.com.cn/tech/2013-12/16/content_17176424.htm.
	NING B. How the “Yutu rover” was designed-the birth of China’s first lunar rover[EB/OL]. (2013-12-16). https://www.chinadaily.com.cn/tech/2013-12/16/content_17176424.htm. (in Chinese)
[72]	潘冬, 贾阳, 袁宝峰, 等. 祝融号火星车主动悬架式移动系统设计与验证[J]. 中国科学: 技术科学, 2022, 52(2): 278-291.
	PAN D, JIA Y, YUAN B F, et al. Design and verification of the active suspension mobility system of the Zhurong Mars rover[J]. SCIENTIA SINICA Technologica, 2022, 52(2): 278-291. (in Chinese)
[73]	新浪网. 倒计时!祝融号将以每秒4.9公里俯冲火星,中国即将获得登火第二[EB/OL]. (2021-05-13). https://k.sina.com.cn/article_2665232937_9edc3a29019012vio.html.
	Sina. Countdown! The Zhu Rong rover will be plunging into Mars at a speed of 4.9 kilometers per second, and China is about to achieve its second landing on Mars[EB/OL]. (2021-05-13). https://k.sina.com.cn/article_2665232937_9edc3a29019012vio.html. (in Chinese)
[74]	ALBERS A, OTTNAD J. Integrated structural and controller optimization for lightweight robot design[C]// Proceedings of the 2009 9th IEEE-RAS International Conference on Humanoid Robots. Paris, France: IEEE, 2009: 93-98.
[75]	张鑫. 基于单环运动链移动机构的跳跃步态规划与分析[D]. 北京: 北京交通大学, 2023.
	ZHANG X. Planning and analysis of jumping gait based on single-loop motion chain moving mechanism[D]. Beijing: Beijing Jiaotong University, 2023. (in Chinese)
[76]	苏波, 闫曈, 许威, 等. 四足机器人高机动越野技术研究[J]. 中国科学: 技术科学, 2023, 53(9): 1574-1588.
	SU B, YAN T, XU W, et al. Research on high mobility off road technology of quadruped robots[J]. Chinese Science: Technical Science, 2023, 53(9): 1574-1588. (in Chinese)
[77]	LEUNG T H Y, IGNATYEV D, ZOLOTAS A. Hybrid terrain traversability analysis in off-road environments[C]// Proceedings of the 2022 8th International Conference on Automation, Robotics and Applications (ICARA). Prague, Czech Republic: IEEE, 2022: 50-56.
[78]	ZÜRN J, BURGARD W, VALADA A. Self-Supervised visual terrain classification from unsupervised acoustic feature learning[J]. IEEE Transactions on Robotics, 2021, 37(2): 466-481.
[79]	WAIBEL G G, LÖW T, NASS M, et al. How rough is the path? Terrain traversability estimation for local and global path planning[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 16462-16473.
[80]	LIU Z X, YUAN X F, HUANG G M, et al. 3D gradient reconstruction-based path planning method for autonomous vehicle with enhanced roll stability[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 20563-20571.
[81]	SHIREL J, AMIR D. Deep reinforcement learning for safe local planning of a ground vehicle in unknown rough terrain[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 6748-6755.
[82]	DANIELSSON S, PETERSSON L. Monte Carlo based threat assessment: analysis and improvements[C]// Proceedings of 2007 IEEE Intelligent Vehicles Symposium. Istanbul, Turkey: IEEE, 2007: 233-238.
[83]	SONG B Y, WANG Z D, ZOU L. On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm[J]. Cognitive Computation, 2017, 9(1): 5-17.
[84]	SUNG I, CHOI B, NIELSEN P. On the training of a neural network for online path planning with offline path planning algorithms[J]. International Journal of Information Management, 2021, 57: 102142.
[85]	WU K Y, WANG H, ESFAHANI M A, et al. Achieving real-time path planning in unknown environments through deep neural networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 23(3): 2093-2102.
[86]	PANOV A, YAKOVLEV K, SUVOROV R. Grid path planning with deep reinforcement learning: preliminary results[J]. Procedia Computer Science, 2018, 123: 347-353.
[87]	WANG Z J, LU G Q, TAN H T. Risk-field based motion planning method for multi-vehicle conflict scenario[J]. IEEE Transactions on Vehicular Technology, 2024, 73(1): 310-322.
[88]	LIAO Y, YU G Z, CHEN P, et al. Integration of decision-making and motion planning for autonomous driving based on double-layer reinforcement learning framework[J]. IEEE Transactions on Vehicular Technology, 2024 73(3): 3142-3158.
[89]	IBARZ J, TAN J, FINN C, et al. How to train your robot with deep reinforcement learning: lessons we have learned[J]. The International Journal of Robotics Research, 2021, 40(4/5): 698-721.
[90]	ORR J, AYAN D. Multi-agent deep reinforcement learning for multi-robot applications: a survey[J]. Sensors, 2023, 23(7): 3625-3661.
[91]	WANG Z Z, KAI Z, GUO D C, et al. Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty[J]. Petroleum Science, 2023, 20(1): 261-276.
[92]	MEHTA B, MANFRED D, FLORIAN G, et al. Active domain randomization[J]. Proceedings of Machine Learning Research, 2020, 100: 1162-1176.
[93]	ARNDT K, MURTAZA H, ALI G, et al. Meta reinforcement learning for sim-to-real domain adaptation[C]// Proceedings 2020 IEEE International Conference on Robotics and Automation (ICRA). Paris, France: IEEE, 2020: 2725-2731.
[94]	KHAIRY S, PRASANNA B. Multi-fidelity reinforcement learning with control variates[J]. Neurocomputing, 2024, 597: 127963.
[95]	MANDERSON T, WAPNICK S, MEGER D, et al. Learning to drive off road on smooth terrain in unstructured environments using an on-board camera and sparse aerial images[C]// Proceedings of 2020 IEEE International Conference on Robotics and Automation (ICRA). Paris, France: IEEE, 2020: 1263-1269.
[96]	AGARWAL R, MAX S, PABLO S, et al. Deep reinforcement learning at the edge of the statistical precipice[J]. Advances in neural information processing systems, 2021, 34: 29304-29320.
[97]	SHI L, GEN L, YUTING W, et al. Pessimistic Q-learning for offline reinforcement learning: towards optimal sample complexity[J]. Proceedings of Machine Learning Research, 2022, 162: 19967-20025.
[98]	LI S E. Deep reinforcement learning. in Reinforcement learning for sequential decision and optimal control[M]. Singapore: Springer, 2023: 365-402.