基于系统响应的履带车辆路面识别方法

为提高履带车辆对不同路面适应能力，基于多体动力学仿真平台建立履带车辆及多种路面动力学仿真模型.通过履带车与路面模型的行驶仿真，采集车体质心动力学响应时域信号，并应用小波变换分解该信号.采用距离评估技术提取上述分解信号的敏感特征向量，利用BP神经网络基于上述敏感特征向量提出路面识别方法.搭建小型履带模型车测试系统，使模型车行驶于实际路面并进行现场测试，采集测试过程中履带模型车车体质心、负重轮及履带板动力学响应时域信号，对提出的路面识别方法进行验证.结果表明，该路面识别方法识别精度达到99%，该方法对路面类型具有高度识别能力.

Road Identification Method for Tracked Vehicles Based on System Response

In order to improve tracked vehicles’ adaptability to different types of road surfaces， a dynamic simulation model of tracked vehicles and various types of road was established based on the multi-body simulation platform. The time-domain dynamic response signals of vehicle centroid were collected through the driving simulation of tracked vehicles and road models， and the signals were decomposed by wavelet transformation. Distance evaluation technique was used to extract sensitive feature vectors. A road identification method based on the above sensitive feature vectors was proposed by using BP neural network. In order to verify the validity of the method， a test system based on small tracked vehicle models was built.The vehicle model drived on the actual road to collect the time-domain dynamic response signals of tracked vehicles′ body centroid， load wheels and track-terrain interaction. The results showed that the identification precision of the method is 99%. This method has a high identiciation ability for road types.