DRIVE Through the Unpredictability: From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty

摘要

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 uncrewed ground vehicles (UGVs), which an onboard sensor can not directly measure. In this work, we propose using the Data-driven Robot Input Vector Exploration (DRIVE) protocol to standardize the data collection 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 h and 14.7km. 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 a skid-steering mobile robots (SSMRs). An unpredictability metric is proposed to quantify a system’s ability to predict the resulting motion of a command with a single scalar, normalized between 0 and 1. Finally, we share our lessons learned on running system identification with a 470 kg UGV to help the community.