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6×6 UGV: STOCHASTIC DYNAMICS FUNDAMENTALS FOR MOBILITY ESTIMATION

机译:6×6 UGV:用于移动性估计的随机动力学基本原理

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摘要

Stochastic modeling for mobility estimation has vastly improved through recent research and the advancements in technology, but those advancements haven't fully been applied to full vehicle mobility control and on-line (real time) analysis of each driving wheel's contribution and influence. This paper presents an analysis of fundamental mobility dynamics implemented into advanced stochastic estimation methodologies. Based on this analysis, the paper formulates: (ⅰ) developed on-line mobility criteria in stochastic conditions from one wheel to full vehicle with six driving wheels in which the contribution of every wheel can be estimated, and (ⅱ) terrain characterization and agile vehicle dynamics information to estimate UGV mobility in real time. This analysis enables on-line mobility estimation for UGVs to make control changes as the event of poor terrain conditions occur (or before it) rather than after the event, causing the vehicle to then optimize its reaction to regain control. These fundamental applications for mobility control in stochastic conditions enable today's one wheel modeling solutions to be applied to the full vehicle.
机译:通过最近的研究和技术进步,用于移动性估计的随机建模已大大改善,但是这些进步尚未完全应用于完整的车辆移动性控制以及每个驱动轮的贡献和影响的在线(实时)分析。本文介绍了在先进的随机估计方法中实施的基本流动性动力学分析。在此分析的基础上,本文提出:(ⅰ)在随机条件下开发了从一个车轮到具有六个驱动轮的完整车辆的在线移动性标准,其中可以估计每个车轮的贡献,以及(ⅱ)地形表征和敏捷性车辆动态信息实时估计UGV机动性。这种分析使UGV的在线迁移率估计能够在不良地形条件发生时(或之前)进行控制更改,而不是在事件发生之后进行更改,从而使车辆优化其反应以重新获得控制。这些用于随机条件下的移动性控制的基本应用使得当今的单轮建模解决方案可以应用于整车。

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