Abstract
Online adaptation of motion models enables autonomous robots to move more accurate in case of unknown disturbances. This paper proposes a new adaptive compensation feedforward controller capable of online learning a compensation motion model without any prior knowledge to counteract non-modeled disturbance such as slippage or hardware malfunctions. The controller is able to prevent motion errors a priori and is well suited for real hardware due to high adaptation rate. It can be used in conjunction with any motion model as only motion errors are compensated. A simple interface enables quick deployment of other robot systems as demonstrated in Small Size and Rescue Robot RoboCup leagues.
Cite
CITATION STYLE
Ommer, N., Stumpf, A., & von Stryk, O. (2018). Real-time online adaptive feedforward velocity control for unmanned ground vehicles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11175 LNAI, pp. 3–16). Springer Verlag. https://doi.org/10.1007/978-3-030-00308-1_1
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