Locally-adaptive slip prediction for planetary rovers using Gaussian processes
- Resource Type
- Conference
- Authors
- Cunningham, Chris; Ono, Masahiro; Nesnas, Issa; Yen, Jeng; Whittaker, William L.
- Source
- 2017 IEEE International Conference on Robotics and Automation (ICRA) Robotics and Automation (ICRA), 2017 IEEE International Conference on. :5487-5494 May, 2017
- Subject
- Robotics and Control Systems
Visualization
Predictive models
Gaussian processes
Adaptation models
Computational modeling
Geometry
Data models
- Language
This paper presents a method for predicting slip using Gaussian process regression. Slip models are learned for visually classified terrain types as a function of terrain geometry. Spatial correlations between terrain properties are leveraged for on-line slip model adaptation. Results show that regression-based modeling using in-situ rover data outperforms the state-of-practice, terrestrially-calibrated slip curves in both mean prediction and uncertainty bounds. Local adaptation improves slip prediction results, particularly in high-slip sand areas that pose the greatest threat to rovers. Slip estimates made using a visual classifier to identify terrain type are compared to estimates using on-line model selection with only proprioceptive slip measurements as inputs. The proprioceptive results nearly match the visual results, showing that this approach could work even when a visual classifier is not available.