Research paper
Learning-based NMPC · Preserving off-road tracking constraints
Online nonparametric models and uncertainty estimates guide a nonlinear MPC controller's linear and angular velocities. The authors report over five kilometres of travel by a 900 kg ground robot while respecting path and localization constraints.
Reviewed 2026-11-05 · Source and bibliography checked
- Original title
- Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking
- Publisher
- SAGE Publications
- Authors
- Chris J. Ostafew, Angela P. Schoellig, Timothy D. Barfoot
- DOI
- 10.1177/0278364916645661
- First online publication
- 2016-07-11
- Source language
- en
- Edition
- The International Journal of Robotics Research; 2016; DOI 10.1177/0278364916645661
- Access
- Publisher abstract · restricted full text
- Publication status
- Published
Related resources and engineering notes
Source overview
Online nonparametric models and uncertainty estimates guide a nonlinear MPC controller's linear and angular velocities. The authors report over five kilometres of travel by a 900 kg ground robot while respecting path and localization constraints.
Use and review
Review uses publisher-deposited abstract and bibliography only. Field results are specific to terrain, localization and model-update conditions, rather than proof of safety for every obstacle or environment.
Sources & editions
Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking ↗
SAGE Publications · The International Journal of Robotics Research; 2016; DOI 10.1177/0278364916645661
IJRR publisher-deposited abstract and online-date registration, DOI 10.1177/0278364916645661.