Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking


  1. Ostafew, C.J.
  2. Collier, J.
  3. Schoellig, A.P.
  4. Barfoot, T.D.
Corporate Authors
Defence Research and Development Canada, Suffield Research Centre, Ralston AB (CAN);Toronto Univ, Downsview ONT (CAN) Inst for Aerospace Studies
This paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm to achieve high-performance path tracking in challenging off-road terrain through learning. The LB-NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modelled as a Gaussian Process (GP) as a function of system state, input, and other relevant variables. The GP is updated based on experience collected during previous trials. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, GPS-denied environments. The paper presents experimental results including over 3 km of travel by three significantly different robot platforms with masses ranging from 50 kg to 600 kg and at speeds ranging from 0.35 m/s to 1.2 m/s.1 Planned speeds are generated by a novel experience-based speed scheduler that balances overall travel time, path-tracking errors, and localization reliability. The results show that the controller can start from a generic a priori vehicle model and subsequently learn to reduce vehicle- and trajectory-specific path-tracking errors based on experience.
model predictive control;computer vision;path following;visual navigation
Report Number
DRDC-RDDC-2015-P024 — External Literature
Date of publication
01 Jun 2015
Number of Pages
Electronic Document(PDF)

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