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Venkataramanan Rajagopalan, Çetin Meriçli, and Alonzo Kelly. Slip-aware Model Predictive optimal control for Path following. In Robotics and Automation (ICRA), 2016 IEEE International Conference on, pp. 4585–4590, 2016.
Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skidsteered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.
@InProceedings{rajagopalan2016slip,
title={Slip-aware Model Predictive optimal control for Path following},
author={Rajagopalan, Venkataramanan and Meriçli, Çetin and Kelly, Alonzo},
booktitle={Robotics and Automation (ICRA), 2016 IEEE International Conference on},
pages={4585--4590},
year={2016},
organization={IEEE},
abstract = {Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skidsteered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.},
bib2html_pubtype = {Refereed Conference},
bib2html_rescat = {Robot Control},
bib2html_dl_pdf = {../files/rajagopalanICRA2016SlipAwareMPOC.pdf},
}
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