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Multisensor-Based Predictive Control for Autonomous Parking
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AI
DOI：10.1109/TRO.2021.3091285.png)
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摘要
En 中文
This article formalizes, under a single common multisensor-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 us 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.
Keyword:
Task analysis
Robot sensing systems
Automobiles
Collision avoidance
Predictive control
Planning
Location awareness
Intelligent parking system
intelligent vehicles
model-predictive control (MPC)
motion control
sensor-based control
期刊
IF:
10.5
论文数:
3.3K
被引数:
2.8W
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