基于地形特征时频变换的无人特种车辆速度自适应控制方法

doi:10.12382/bgxb.2023.0823

摘要/Abstract

摘要：

为提高复杂环境下无人特种车辆安全性、自主性及作业能力,面向在崎岖地形下无人特种车辆应用需求,提出一种基于地形特征时频变换的速度自适应控制方法。通过度量地形特征崎岖度、建立崎岖度和车速数学模型,实现无人特种车辆在崎岖地形的自主、自适应车速规划。针对崎岖地形和坡度引起的点云数据失真问题,融合惯性测量单元传感器数据对点云进行补偿,获得了车辆前方地形的精准点云数据;针对可视距离与跟踪精度冲突问题,不同于传统横向曲率计算方式,采用以线到面的方式,将激光雷达纵向剖面点云数据进行时频变换后,在频域内选取次频区域的积分面积作为崎岖度量化值,实现对不同地形下的崎岖度量化;此外,基于上述获得的崎岖度,采用迭代搜索的方式建立速度与崎岖度数学模型,并采用滑动窗口的方式更新崎岖度,实现车速到崎岖度之间的连续映射。以可控震源野外勘探特种车为研究对象,采用上述方法在实际野外地形环境中进行多次实验。实验结果表明,所提出的方法在崎岖地形具有良好的安全性、自主性,可以识别地形和自适应控制车速。

关键词: 地形识别, 纵向剖面, 频域法, 崎岖度量化, 速度自适应控制

Abstract:

To ensure the safety, autonomy, and mobility of unmanned special vehicles in complex environments, a speed-adaptive control method based on terrain feature time-frequency transform is proposed for navigating the unmanned special vehicles on rugged terrains. The autonomy and adaptive speed planning of unmanned special vehicles on rugged terrains is achieved by measuring the ruggedness of terrain and establishing a continuous mathematical model of terrain ruggedness and vehicle speed. The point cloud data is corrected through the fusion of inertial measurement unit (IMU) sensor data. This correction ensures the precision of the point cloud data in front of the vehicle, addressing the issues arising from rocky terrain and slopes. Subsequently, a line-to-surface approach is employed to quantify the ruggedness across various terrains, which is diferent from the traditional transverse curvature calculation. The ruggedness value is determined by choosing the integrated area of the sub-frequency region in the frequency domain after the time-frequency transformation of LIDAR longitudinal profile point cloud data. Moreover, a mathematical model for speed and terrain ruggedness is established through an iterative search based on the quantified ruggedness values. The ruggedness value is continuously updated using a sliding window, facilitating the seamless mapping between vehicle speed and terrain ruggedness. The proposed method is then validated utilizing a seismic vibrator vehicle as the research subject through a series of experiments conducted in actual field terrain environments. The experimental results affirm the effectiveness of the proposed method in terrain identification and adaptive vehicle speed control.

Key words: terrain identification, longitudinal profile, frequency domain method, roughness quantification, speed adaptive control

中图分类号:

TJ301

王亮, 汪首坤, 牛天伟, 王军政. 基于地形特征时频变换的无人特种车辆速度自适应控制方法[J]. 兵工学报, 2024, 45(10): 3718-3731.

WANG Liang, WANG Shoukun, NIU Tianwei, WANG Junzheng. Speed Control Method for Unmanned Special Vehicle Based on Terrain Feature Time-frequency Transform[J]. Acta Armamentarii, 2024, 45(10): 3718-3731.

图/表 25

图1 基于频域内地形特征的特种车速度自适应控制

Fig.1 Speed adaptive control based on terrain features in frequency domain

图2 激光线束等间隔划分

Fig.2 Equidistant division of laser beams

图3 对segment进行单元栅格划分

Fig.3 Unit grid division of segments

图4 车辆速度自适应调整策略

Fig.4 Vehicle speed a adaptive djustment strategy

图5 模拟复合式地形纵向剖面点云z值

Fig.5 z value map of simulated composite terrain longitudinal section point cloud

图6 不同采样频率对应幅频特性

Fig.6 Amplitude-frequency characteristic curves corresponding to different sampling frequencies

图7 不同采样频率对应功率谱密度

Fig.7 Power spectral density curves corresponding to different sampling frequencies

图8 不同采样频率对应崎岖度变化曲线图

Fig.8 Different sampling frequencies corresponding to the change curves of roughness

图9 车载激光地形扫描实验平台

Fig.9 Vehicle-mounted laser terrain scanning experimental platform

图10 硬件连接以及通信方式

Fig.10 Hardware connection and communication method

表1 实验平台参数表

Table 1 Experimental platform parameters

参数	数值
震源车长度/m	10.0
震源车宽度/m	3.38
震源车高度/m	3.6
震源车前后轴距/m	4
激光雷达距地高度/m	1.4
激光雷达扫描频率/Hz	20
激光雷达水平扫描范围/(°)	360
激光雷达垂直扫描范围/(°)	-55~15
激光雷达有效测量距离/m	150
GPS频率/Hz	20
GPS定位精度/m	0.01
IMU频率/Hz	50
IMU精度/(°)	0.05

图11 实验所采集复合式地形实景

Fig.11 Composite terrain real scene collected by the laboratory

图12 所扫描的崎岖地形及其点云图

Fig.12 Scanned rugged terrain and its point cloud image

图13 3种地形的纵向剖面点云z值

Fig.13 z-value curves of the longitudinal section point clouds of three terrains

图14 地形纵向剖面点云z值幅频特性

Fig.14 Amplitude-frequency characteristic curve of z value of terrain longitudinal profile point cloud

图15 地形纵向剖面点云z值功率谱密度

Fig.15 z-value power spectral density curve of terrain longitudinal profile point cloud

图16 地形崎岖度变化

Fig.16 Change curve of terrain roughness

表2 3种地形的崎岖度阈值范围

Table 2 Ruggedness for 3 types of terrain

地形	S
平坦地形	S<0.09
较平坦地形	0.09≤S≤0.17
崎岖地形	S>0.17

图17 野外平坦土路纵向剖面点云z值

Fig.17 z-value curve of longitudinal profile point cloud of field flat dirt road

图18 野外平坦土路崎岖度变化

Fig.18 Variation curve of field flat dirt road roughness

图19 野外平坦土路崎岖度随车速自适应变化

Fig.19 Adaptive change curve of field flat dirt road roughness with vehicle speed

图20 前车体车速随距离自适应变化

Fig.20 Adaptive variation curve of front vehicle body speed with distance

图21 后车体车速随距离自适应变化

Fig.21 Adaptive variation curve of rear vehicle speed with distance

图22 车体各部位车速自适应变化

Fig.22 Speed adaptive change curve of each part of vehicle body

图23 目标车速随距离自适应变化

Fig.23 Adaptive change curve of target speed with distance

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