An accurate motion model is a fundamental component of most autonomous navigation systems. While much work has been done on improving model formulation, no standard protocol exists for gathering empirical data required to train models. In this work, we address this issue by proposing Data-driven Robot Input Vector Exploration (DRIVE), a protocol that enables characterizing uncrewed ground vehicles (UGVs) input limits and gathering empirical model training data. We also propose a novel learned slip approach outperforming similar acceleration learning approaches. Our contributions are validated through an extensive experimental evaluation, cumulating over 7 km and 1.8 h of driving data over three distinct UGVs and four terrain types. We show that our protocol offers increased predictive performance over common human-driven data-gathering protocols. Furthermore, our protocol converges with 46 s of training data, almost four times less than the shortest human dataset gathering protocol. We show that the operational limit for our model is reached in extreme slip conditions encountered on surfaced ice. DRIVE is an efficient way of characterizing UGV motion in its operational conditions. Our code and dataset are both available online at this link: https://github.com/norlab-ulaval/DRIVE.

翻译：精确的运动模型是大多数自主导航系统的基本组成部分。尽管已有大量研究致力于改进模型公式化，但目前仍缺乏用于收集训练模型所需经验数据的标准协议。本研究通过提出数据驱动的机器人输入向量探索（DRIVE）协议来解决这一问题，该协议能够表征无人地面车辆（UGV）的输入极限并收集经验模型训练数据。我们还提出了一种新型学习型滑移方法，其性能优于类似的加速度学习方法。通过涵盖三款不同UGV和四种地形类型、累计超过7公里及1.8小时驾驶数据的广泛实验评估，我们验证了研究成果。结果表明，与常见的人工驾驶数据采集协议相比，本协议能提供更优的预测性能。此外，本协议仅需46秒训练数据即可收敛，所需数据量几乎是最短人工数据集采集协议的四分之一。研究显示，在冰面极端滑移条件下，本模型将达到操作极限。DRIVE是在实际工况下表征UGV运动特性的高效方法。我们的代码与数据集均可在以下链接获取：https://github.com/norlab-ulaval/DRIVE。