编队机动自适应车间距保持控制

doi:10.12382/bgxb.2023.0878

摘要/Abstract

摘要：

编队机动是地面无人平台自主导航技术的重要功能之一。研究编队机动系统的车速自适应车间距保持控制方法,建立车速自适应车间距控制框架,将车间距保持控制分为串联的3个部分:跟驰模型、车间距控制和速度规划。以传统时距模型为基础,引入前后车速度关系的修正项,提出改进非线性跟驰模型,模型符合编队机动逻辑,根据车速变化自适应调节安全车间距,提高编队效率。以距离误差和速度误差的加权组合作为车间距控制的目标,设计加权系数的自修正PID控制器,提高了控制响应和控制精度。以参考速度和前后车运动特性为约束,设计速度规划器,对期望速度进行精确跟踪。通过仿真实验和实车实验验证车间距保持控制的性能,实验结果表明控制算法对不同速度具有较好的控制效果,在越野环境前车频繁加减速的工况下具有较好的跟驰性能,且自动适应速度变化过程。

关键词: 地面无人平台, 编队机动, 自适应车间距控制, 跟驰模型, 速度规划

Abstract:

Formation maneuver is one of the important functions of autonomous navigation technology for unmanned ground platforms.A novel velocity-adaptive inter-vehchiled istance control method is proposed for unmanned ground vehicle (UGV) convoy, and a cascaded adaptive inter-vehchile distance control diagram is introduced,which includes following model, inter-distance control and velocity planning. A modified nonlinear following model which considers relative velocity error is established based on the traditional time-headway policy. Then a self-tuned PID controller is calculated using the weighed combination of distance error and velocity error. The control performance is validated through simulation and field test. The results show that the proposed inter-distance control method is adaptive to velocity change, and improves the overall control performance of UGV convoy in unstructured terrain.

Key words: unmanned ground vehicle, adaptive inter-distance control, following model, velocity planning

中图分类号:

TP242.3

赵熙俊, 崔星, 李兆冬, 王一全, 杨雨. 编队机动自适应车间距保持控制[J]. 兵工学报, 2023, 44(S2): 44-51.

ZHAO Xijun, CUI Xing, LI Zhaodong, WANG Yiquan, YANG Yu. Adaptive Inter-vechile Distance Control for Unmanned Ground Vehicle Convoy[J]. Acta Armamentarii, 2023, 44(S2): 44-51.

图/表 11

图1 自适应车距保持控制框图

Fig.1 Adaptive inter-distance control

图2 线性时距与改进时距模型对比

Fig.2 Comparison between linear time interval model and improved time interval model

图3 车间距控制算法框图

Fig.3 Inter-distance control scheme

图4 速度规划

Fig.4 Velocity planning

图5 频繁加减速工况仿真结果

Fig.5 Simulated results

图6 编队机动实验场景

Fig.6 Field test environment

图7 两车编队系统中车间距控制实验结果(轮式)

Fig.7 Test results of inter-vechile distance control for two convoy systems(wheeled platform)

图8 两车编队机动系统中车间距控制实验结果(履带式)

Fig.8 Test results of inter-vechile distance control for two convoy systems(tracked platform)

图9 三车编队系统中车间距控制实验结果

Fig.9 Test results for inter-vechile distance control of three convoy systems

图10 编队机动实验路径

Fig.10 Path of formation maneuver test

表1 全流程实验结果

Table 1 Resultsofthe whole process test

对比参数	最大车间距/m	25km/h车速下车间距方差/m	急停车间距/m
改进前	26.73	6.743	7.0
改进后	18.43	1.213	9.8

参考文献 20

[1]	张筱镡. 领航-跟随型编队机动控制方法研究[D]. 长沙: 国防科技大学, 2022:1-12.
	ZHANG X T. Research on leader-follower formation maneuver control[D]. Changsha: National University of Defense Technology, 2022:1-12. (in Chinese)
[2]	SCHOENHERR E, THEISEN B L, ANIMASHAUN A, et al. Convoy active safety technologies warfighter experiment I[C]//Proceedings of the Unmanned Systems Technology X. Orlando, FL, US:SPIE, 2008:69621N.
[3]	SCHOENHERR E. Convoy active safety technologies warfighter experiment II[C]// Proceeding of the SPIE Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques. San Jose, CA, US: SPIE, 2009: 72520K.
[4]	LI T N, CHEN D J, ZHOU H, et al. Fundamental diagrams of commercial adaptive cruise control: worldwide experimental evidence[J]. Transportation Research Part C: Emerging Technologies, 2022, 134(1):103458. doi: 10.1016/j.trc.2021.103458 URL
[5]	ANUMOLA V, PAVAN R, VADDE K. Implementation of adaptive cruise control and cloud based black box technology for modern automotive vehicles[C]//Proceedings of the 2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics. Ballar, India:IEEE, 2023:1-6.
[6]	陈慧岩, 张玉. 军用地面无人机动平台技术发展综述[J]. 兵工学报, 2014, 35(10):1696-1706. doi: 10.3969/j.issn.1000-1093.2014.10.026
	CHEN H Y, ZHANG Y. An overview of research on military unmanned ground vehicles[J]. Acta Armamentarii, 2014, 35(10):1696-1706. (in Chinese)
[7]	郭孔辉. 非结构环境下智能车辆自主跟随前车控制方法研究[D]. 北京: 北京理工大学, 2020:1-13.
	GUO K H. research on the method of intelligent vehicle following preceding vehicle in the unstructured environment[D]. Beijing: Beijing Institute of Technology, 2020:1-13. (in Chinese)
[8]	王渤谦. 非结构化环境下无人车路径规划与编队控制研究[D]. 哈尔滨: 哈尔滨工业大学, 2022:1-11.
	WANG B Q. Research on path planning and formation control of unmanned vehicles in unstructured environment[D]. Harbin:Harbin Institute of Technology, 2022:1-11. (in Chinese)
[9]	WANG Q, DUAN X T, XIA H Y, et al. A multi-agent autonomous formation motion planning method or car-l; ike robots based on the leader-follower model[C]// Proceedings of International Conference on Autonomous Unmanned Systems.Xi’an, China: Springer Science and Business Media Deutschland GmbH, 2023:1471-1481.
[10]	CHAOCHEN Z L, WANG Y N, TAN H R, et al. Posture-based leader-follower formation for multiple mobile robot systems using data-based adaptive iterative learning control[C]// Proceedings of 2022 Chinese Automation Congress.Xiamen, China:IEEE, 2022:6463-6468.
[11]	YU L L, WANG Z J, KUANG Z X. Hierarchical platoon motion planning strategy based on leader-follower structure[C]// Proceedings of 2021 IEEE International Conference on Robotics and Biomimetics.Sanya, China:IEEE, 2021:1599-1604.
[12]	JOHN-JAIRO M, CARLOS C. A safe longitudinal control for adaptive cruise control and stop-and go scenarios[J]. IEEE Transaction on Control Systems Technology, 2007, 15(2): 246-258. doi: 10.1109/TCST.2006.886432 URL
[13]	ALI A, MARTINET P. Minimizing the inter-vehicle distances of the time headway policy for urban platoon control with decoupled longitudinal and lateral control[C]// Proceedings of the 16 the IEEE Conference on Intelligent Transportation Systems. The Hague, the Netherlands: IEEE 2013: 1805-1810.
[14]	倪捷, 张凯铎, 刘志强, 等. 自适应期望跟车间距和行为习惯的驾驶人跟驰模型[J]. 交通运输系统工程与信息, 2022, 22(3):286-292, 302.
	NI J, ZHANG K D, LIU Z Q, et al. Car-following model with adaptive expected driver’s following distance and behavior[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3):286-292, 302. (in Chinese)
[15]	栾军超, 刘灿昌, 张鑫越, 等. 基于三维模糊控制的车辆自适应巡航间距策略[J]. 科学技术与工程, 2022, 22(14):5906-5913.
	LUAN J C, LIU C C, ZHANG X Y, et al. Adaptive cruise control spacing strategy for vehicle based on three-dimensional fuzzy control[J]. Science Techonology and Engineering, 2022, 22(14):5906-5913. (in Chinese)
[16]	MARIUS C, ALEXANDRU G, DRAGOS T, et al. Comparative analysis of driver’s motion using different headrest positions during the rear-end collision[J]. IOP Conference Series: Materials Science and Engineering, 2022, 1220(1): 12-26.
[17]	薛晴婉, 徐嘉伟. 雾天驾驶人车辆操纵行为特性及其与追尾风险相关性分析[J]. 交通信息与安全, 2022, 40(1): 19-27.
	XUE Q W, XU J W. A study on the correlation between vehicle control behaviors and rear-end collision risk under foggy conditions[J]. Journal of Transport Information and Safety, 2022, 40(1): 19-27. (in Chinese)
[18]	WANG Z L, SHI Y Y, TONG W P, et al. Car-following models for human-driven vehicles and autonomous vehicles: a systematic review[J]. Journal of Transportation Engineering, Part A: Systems, 2023, 149(8):1-15.
[19]	丁晓海, 郭戈. 车队控制中的一种通用可变时距策略[J]. 自动化学报, 2019, 45(7):1335-1343.
	DING X H, GUO G. A general variable time headway policy in platoon control[J]. Acta Automatica Sinica, 2019, 45(7):1335-1343. (in Chinese)
[20]	ZHAO X J, LIU J, ZHU S, et al. A motion planning method for autonomous vehicles[C]// Proceeding of the International Conference on Control and Automation. Kathmandu, Nepal: IEEE, 2016:1-6.