DRIVE: Data-driven Robot Input Vector Exploration

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 7km and 1. 8h 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.

Authors

• Dominic Baril
• Simon-Pierre Deschênes
• Luc Coupal
• Cyril Goffin
• Julien Lépine
• Philippe Giguère
• François Pomerleau

Keywords

• Training
• Adaptation models
• Protocols
• Training data
• Predictive models
• Vectors
• Data models
• Input Vector
• Standard Protocol
• Predictive Performance
• Empirical Data
• Motion Model
• Terrain Types
• Extensive Experimental Evaluation
• Dynamic Model
• Training Dataset
• Prediction Error
• Angular Velocity
• Centrifugal Force
• Model-based Approach
• Input Space
• Bayesian Regression
• Quasi-steady State
• Longitudinal Velocity
• Motion Prediction
• Global Frame
• Warthogs
• Wheel Velocity
• Body Velocity
• Slip Velocity
• Vehicle Acceleration
• Slip Model
• Iterative Closest Point
• Median Decrease
• Entire Input
• Learning Models