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Multi-Sensor-Based Predictive Control For Autonomous Parking

Abstract : This paper formalizes, under a single common Multi-Sensor-Based Predictive Control framework, five different types of parking maneuvers: perpendicular, diagonal for both forward and backward motions and parallel for backward motions. Since, from a practical point of view, forward parallel parking is usually not advisable, it is not addressed in this work. By moving the effort from motion planning to control, the parking tasks can be completely defined solely from the detected empty parking spots. Additionally, the classical compromise between completeness and computational efficiency when compared to exploration-based path planning techniques is eliminated. The results of a few individual cases are presented and compared against a state of the art path planning approach to illustrate the behavior and performance of the proposed framework as well as results from exhaustive simulations to assess its convergence. As shown in the convergence analyses, the presented approach allows to park from virtually any sensible initial pose. Finally, real experimentation using a robotized Renault ZOE shows the validity and robustness in the convergence domain of the presented approach.
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Contributor : Olivier Kermorgant Connect in order to contact the contributor
Submitted on : Wednesday, July 14, 2021 - 4:08:55 PM
Last modification on : Monday, June 27, 2022 - 3:07:08 AM
Long-term archiving on: : Friday, October 15, 2021 - 4:17:05 PM


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David Pérez-Morales, Olivier Kermorgant, Salvador Domínguez-Quijada, Philippe Martinet. Multi-Sensor-Based Predictive Control For Autonomous Parking. IEEE Transactions on Robotics, IEEE, In press, ⟨10.1109/ICARCV50220.2020.9305465⟩. ⟨hal-03286432⟩



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