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核心组成部分。尽管已有大量研究致力于改进模型构建，但目前尚无标准协议来收集训练模型所需的经验数据。本文通过提出数据驱动的机器人输入向量探索（DRIVE）协议来应对该问题，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颖的学习型滑移方法，其性能优于同类加速度学习方法。通过累计超过7公里、1.8小时行驶数据的广泛实验评估（涵盖三种不同UGV和四种地形类型），我们验证了相关贡献。结果表明，与常见的人工驾驶数据收集协议相比，本协议具有更高的预测性能。此外，本协议仅需46秒训练数据即可收敛，这一数据量比最短的人工数据采集协议少近四倍。在冰面极端滑移条件下，我们的模型达到了操作极限。DRIVE是表征UGV在操作条件下运动特性的有效方法。我们的代码与数据集均通过此链接在线提供：https://github.com/norlab-ulaval/DRIVE。