Accession Number : AD1000891


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


Descriptive Note : Technical Report


Corporate Author : Defence Research and Development Canada Suffield, Alberta Canada


Personal Author(s) : Ostafew,Chris J ; Collier,Jack ; Schoellig,Angela P ; Barfoot,Timothy D


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1000891.pdf


Report Date : 01 Jul 2015


Pagination or Media Count : 34


Abstract : This paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC)algorithm to achieve high-performance path tracking in challenging off-road terrain throughlearning. The LB-NMPC algorithm uses a simple a priori vehicle model and a learneddisturbance model. Disturbances are modelled as a Gaussian Process (GP) as a function ofsystem state, input, and other relevant variables. The GP is updated based on experiencecollected 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-deniedenvironments. The paper presents experimental results including over 3km of travel bythree significantly different robot platforms with masses ranging from 50 kg to 600 kg andat speeds ranging from 0.35 m/s to 1.2 m/s.1 Planned speeds are generated by a novelexperience-based speed scheduler that balances overall travel time, path-tracking errors,and localization reliability. The results show that the controller can start from a generica priori vehicle model and subsequently learn to reduce vehicle- and trajectory-specificpath-tracking errors based on experience.


Distribution Statement : APPROVED FOR PUBLIC RELEASE