动态可重构自适应无人车辆编队系统

doi:10.12382/bgxb.2023.0879

摘要/Abstract

摘要：

为研究无人车编队系统的编队保持、队形重构及队形变换功能,提出一种混合式领航跟随策略,以降低对领航车的依赖并确保编队完整性。开发基于车间(Vehicle to Vehicle,V2V)通信的跟随车独立避障功能,并设计了实时管理编队成员属性并支持人机交互的编队节点管理系统。提出一种三维空间下的三次样条曲线动态扩展轨迹规划方法,结合V2V通信获取前车位姿信息生成跟随轨迹并实现避障。利用Frenet坐标系解耦车距保持与轨迹跟踪问题,采用PID控制器和线性二次调节(Linear Quadratic Regulator,LQR)控制器分别进行纵向控制和横向轨迹跟踪。研究结果表明:所搭建的仿真环境可快速验证方法性能,显示该方法具有良好的性能;通过实车验证了车辆编队系统的3种功能,通过车距稳定保持,证实所提方法具备良好实时性,能够实现多车编队的有效跟随,通过多种队形的变换以及成员入队离队场景,显示出高度的智能拓展性和适应性。

关键词: 无人车辆, 编队控制, 车间通信, 轨迹规划, 轨迹跟踪

Abstract:

The formation keeping, reconfiguration and transformation functions of unmanned ground vehicle (UGV) formation systems are studied A hybrid leader-follower strategy is proposed to reduce the dependence on the leading vehicle and ensure the formation integrity. An independent obstacle avoidance function based on vehicle-to-vehicle (V2V) communication for the following vehicles is developed, and a formation node management system is designed manages the attributes of formation members in real time and supports the human-computer interaction. A dynamic extended trajectory planning method with cubic spline curve in three-dimensional space is proposed to generate the following trajectory and realize the obstacle avoidance by acquiring the position information of the front vehicle through V2V communication. The Frenet coordinate system is utilized to decouple the distance keeping and trajectory tracking problems, and the proportional-integral-differential (PID) controller and linear quadratic regulator (LQR) controller are used for longitudinal control and lateral trajectory tracking, respectively. The research results show that the performance of the proposed method can be quickly verifued in the simulation environment built, showing that the method has good performance. And the three functions of the vehicle formation system are verified by real vehicles, and the proposed method is confirmed to have good real-time performance through the stable maintenance of the distance between the vehicles, which is capable of realizing the effective following of the multi-vehicle formation, and shows a high degree of intelligent expansion and adaptability through the transformation of multiple formation shapes as well as the scenarios of members’ joinning and departuring from the vehicle formation.

Key words: unmanned ground vehicle, formation control, vehicle-to-vehicle communication, trajectory planning, trajectory tracking

中图分类号:

U461

贾一飞, 蒋朝阳. 动态可重构自适应无人车辆编队系统[J]. 兵工学报, 2024, 45(10): 3654-3673.

JIA Yifei, JIANG Chaoyang. Dynamic Reconfigurable Adaptive UGV Formation System[J]. Acta Armamentarii, 2024, 45(10): 3654-3673.

图/表 43

图1 编队协同等级分类

Fig.1 Class classification of formation cooperation

表1 多车编队控制架构优缺点

Table 1 Advantages and disadvantages of multi-vehicle formation control architecture

控制架构	优点	缺点
集中式	结构简单便于设计;协调能力强;全局最优	容错性差,过于依赖中央处理单元;计算量大,实时性差
分布式	环境适应性强,行为灵活;计算量小,实时性强	局部最优;性能严重依赖通信质量;整体协调能力弱
混合式	协调能力强;全局最优;环境适应性强	实现难度大,设计复杂

图2 虚拟结构法

Fig.2 Virtual structure approach

图3 基于行为法

Fig.3 Behaviour-based approach

图4 领航跟随法

Fig.4 Leading-following method

表2 不同编队策略应对主要编队问题的能力

Table 2 Ability of different formation strategies to deal with major formation problems

方法	保持	避障	重构	抗通信	实时性
虚拟结构法	优	差	差	差	差
基于行为法	差	优	良	良	优
领航跟随法	优	差	良	差	优
图论法	优	良	优	差	差
人工势场法	良	优	差	优	优

图5 Frenet坐标系与笛卡尔坐标系对照

Fig.5 Contrast between Frenet coordinate system and Cartesian coordinate system

图6 基本样条曲线控制点

Fig.6 Control points of basic spline curve

图7 样条曲线的动态扩展

Fig.7 Dynamic extension of spline curves

图8 Frenet坐标系下的纵向车距

Fig.8 Longitudinal gap in Frenet coordinate system

图9 车辆横向自行车模型

Fig.9 Vehicle lateral bicycle model

图10 横向误差模型

Fig.10 Lateral error model

图11 节点管理系统

Fig.11 Node management system

图12 成员离队场景示意图

Fig.12 Schematic diagram of member departure scenario

图13 成员离队流程

Fig.13 Member departure flowchart

图14 成员入队场景示意图

Fig.14 Scenario of members joinning the vehicle formation

图15 成员入队流程

Fig.15 Flowchart of members joinning the vehicle formation

图16 自适应队形生成示意图

Fig.16 Schematic diagram of adaptive formation generation

图17 监控操纵界面

Fig.17 Monitoring and manipulating interface

表3 编队保持仿真参数表

Table 3 Parameter table of formation keeping simulation

参数	数值
起始间隔/m	1.0
期望车间距/m	1.5
最大车速/(m·s^-1)	0.8

图18 无障碍环境下编队轨迹

Fig.18 Formation trajectories in obstacle-free environment

图19 无障碍环境编队车间距误差

Fig.19 Gap errors of vehicles in obstacle-free environment

图20 无障碍环境编队实时速度

Fig.20 Speed profile of vehicle formation in obstacle-free environment

图21 编队保持速度对比

Fig.21 Comparison of the speed profiles of formation keeping

图22 编队保持间距误差对比

Fig.22 Comparison of spap errors of formation keeping

图23 障碍物环境下编队轨迹

Fig.23 Formation trajectory in obstacle environment

图24 障碍物环境编队实时车间距误差

Fig.24 Gap errors of vehicles in obstacle environment

图25 障碍物环境编队实时速度

Fig.25 Speed profile of vehicle formation in obstacle environment

图26 传统领航跟随法障碍区域行驶

Fig.26 Driving of vehicle formation presented in the traditional leading-following method in the obstacle area

图27 本文方法障碍区域行驶

Fig.27 Driving of vehicle formation presented in the proposed method in the obstacle area

表4 队形重构仿真参数

Table 4 Parameter table of formation reconfiguration simulation

参数	数值
起始间隔/m	1.0
期望车间距/m	1.5
巡航车速/(m·s^-1)	0.3

图28 队形重构车间距误差

Fig.28 Gap error of vehicles in formation reconfiguration

图29 队形重构速度

Fig.29 Speed profile in formation reconfiguration

图30 队形重构场景

Fig.30 Formation changing scenario diagram

图31 保护型队形变换场景

Fig.31 Protection formation changing scenario

图32 保护型队形数据

Fig.32 Protective formation changing scenario data graph

图33 雁型队形变换场景

Fig.33 Geese-flying formation changing scenario

图34 雁型队形数据

Fig.34 Changing curves of geese-flying formation

图35 连续队形变换场景

Fig.35 Continuous formation change scene

图36 实验场地

Fig.36 Experimental site

表5 队形重构实验参数

Table 5 Parameter table of experimental formation reconfiguration

参数	数值
期望车间距/m	1.5
人工平均巡航车速/(m·s^-1)	0.3
出入队阶段临时间距/m	4.
汇入横向误差判断阈值/m	0.2
汇入横摆角判断阈值/(°)	15

图37 队形重构场景实验数据

Fig.37 Experimental data graph of formation reconfiguration scenario

图38 队形变换场景实验数据

Fig.38 Experimental data graph of formation changing scenario

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