提出标准化协议与不确定性度量，提升越野机器人运动预测可靠性
DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty
- 设计DRIVE协议采集多地形多载荷数据，构建滑移状态空间
- 验证了命令速度与稳态滑移的传递函数，实现4.9小时14.7公里实测
- 提出可评估部署风险的命令不确定性度量，适合大型无人车系统研究
越野自主导航面临挑战，主要源于运动模型对地形与无人车（UGV）交互预测能力不足。本文提出使用DRIVE协议标准化采集系统辨识数据并表征滑移状态空间。通过两个平台（75至470公斤）在六种地形（沥青、草地、碎石、冰面、泥地、沙地）上采集共计4.9小时、14.7公里的数据进行验证。利用该数据集，评估了DRIVE协议在探索命令速度空间及识别地形-机器人交互可达速度方面的表现。研究了命令速度空间与稳态滑移之间的传递函数，并提出一种不确定性度量以估计命令不确定性，辅助评估部署中的风险概率与严重性。最后分享了在大型UGV上开展系统辨识的经验教训，供社区参考。
原文摘要 · Abstract (English)
Off-road autonomous navigation is a challenging task as it is mainly dependent on the accuracy of the motion model. Motion model performances are limited by their ability to predict the interaction between the terrain and the UGV, which an onboard sensor can not directly measure. In this work, we propose using the DRIVE protocol to standardize the collection of data for system identification and characterization of the slip state space. We validated this protocol by acquiring a dataset with two platforms (from 75 kg to 470 kg) on six terrains (i.e., asphalt, grass, gravel, ice, mud, sand) for a total of 4.9 hours and 14.7 km. Using this data, we evaluate the DRIVE protocol's ability to explore the velocity command space and identify the reachable velocities for terrain-robot interactions. We investigated the transfer function between the command velocity space and the resulting steady-state slip for an SSMR. An unpredictability metric is proposed to estimate command uncertainty and help assess risk likelihood and severity in deployment. Finally, we share our lessons learned on running system identification on large UGV to help the community.
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