地面车辆机动性评估方法与应用

doi:10.13229/j.cnki.jdxbgxb20210893

摘要/Abstract

摘要：

首先，讨论了地面车辆机动性的内涵，对基于经验、半经验、数值模拟以及机器学习等当前地面车辆机动性评估的主要方法进行了全面、系统的总结，并对比分析了各种方法的优缺点。然后，从军用领域、海底作业、行星探索以及农业作业等机动性评估的应用场景进行了分析讨论。最后，针对目前车辆机动性评估方法中存在的一些问题，从车辆机动性实时评估和地形实时感知、变形地形上的路径规划以及无人系统自主机动性评估等关键技术和应用方面提出了一些探索性的研究方向，为车辆机动性评估方法的后续发展提供了有益的参考。

关键词: 车辆工程, 地面力学, 机动性评估, 越野, 数值模拟

Abstract:

Firstly， the definition of mobility was discussed， and then the main existing ground vehicle mobility evaluation methods， i.e.， empirical model， semi-empirical model， numerical simulation and machine learning， were analysed and summarized comprehensively， also the advantages and disadvantages of each method were compared. In order to describe the vehicle mobility completely， application of these methods are discussed，such as military vehicles， sea-floor operation， planetary exploration and agricultural vehicles. Finally， according to the problems existing in the vehicle mobility evaluation methods， this paper proposed some key technologies and exploratory research directions from real-time evaluation of vehicle mobility and real-time terrain perception， path planning on deformed terrain and autonomous mobility evaluation for unmanned systems， so as to provide a beneficial reference to the development of vehicle mobility elevation methods.

Key words: vehicle engineering, terramechanics, mobility evaluation, off-road, numerical simulation

中图分类号:

U461.5

华琛,牛润新,余彪. 地面车辆机动性评估方法与应用[J]. 吉林大学学报(工学版), 2022, 52(6): 1229-1244.

Chen HUA,Run-xin NIU,Biao YU. Methods and applications of ground vehicle mobility evaluation[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1229-1244.

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地表条件	平均最大应力/（kN·m^-2）
地表条件	理想值（多次通过）	良好的	最大允许值（大多数可在一次通过情况下通过）
温带浸粒烟土	150	200	300
热带浸粒烟土	90	140	240
沼泽地	30	50	60
沼泽地流动层/欧洲沼泽区	5	10	15
雪地	10	25~30	40

评估指标	Lagrangian/ALE FEM	Eulerian FEM	DEM	SPH	MPM
总计	27	36	47	42	40
土壤形变范围	4	9	9	9	9
嵌入障碍物能力	3	7	9	9	9
车辆交互保真度	5	6	8	8	8
仿真计算速率	5	7	6	5	9
实验验证精度	5	3	7	5	3
当前应用趋势	5	4	8	6	5

方法	评估手段	评估对象	方法局限性	应用前景
经验方法	实地试验	常见轮式、履带式车辆	实地测试数据泛化性差	较差
半经验方法	实地试验和理论推导	不受限制	半经验公式存在模型简化与假设	一般
数值模拟法	理论推导和计算机仿真	不受限制	仿真时间较长、建模难度较大	较好
机器学习法	训练数据和机器学习算法	不受限制	训练数据获取困难，泛化性差	好

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