2024-03-29T13:12:41Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1590012023-03-02T13:33:18Zcom_10234_7035com_10234_9col_10234_8617
Repositori UJI
author
Mora, Marta Covadonga
author
Tornero, Josep
2016-04-27T12:25:55Z
2016-04-27T12:25:55Z
2014-12-08
MORA, Marta C.; TORNERO, Josep. Predictive and Multirate Sensor-Based Planning Under Uncertainty. Intelligent Transportation Systems, IEEE Transactions on, 2015, vol. 16, no 3, p. 1493-1504.
1524-9050
http://hdl.handle.net/10234/159001
http:\\dx.doi.org/10.1109/TITS.2014.2366974
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In this paper, a general formulation of a predictive and multirate (MR) reactive planning method for intelligent vehicles (IVs) is introduced. The method handles path planning and trajectory planning for IVs in dynamic environments with uncertainty, in which the kinodynamic vehicle constraints are also taken into account. It is based on the potential field projection method (PFP), which combines the classical potential field (PF) method with the MR Kalman filter estimation. PFP takes into account the future object trajectories and their associated uncertainties, which makes it different from other look-ahead approaches. Here, a new PF is included in the Lagrange-Euler formulation in a natural way, accounting for the vehicle dynamics. The resulting accelerations are translated into control inputs that are considered in the estimation process. This leads to the generation of a local trajectory in real time (RT) that fully meets the constraints imposed by the kinematic and dynamic models of the IV. The properties of the method are demonstrated by simulation with MATLAB and C++ applications. Very good performance and execution times are achieved, even in challenging situations. In a scenario with 100 obstacles, a local trajectory is obtained in less than 1 s, which is suitable for RT applications.
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Dynamics
intelligent vehicles
Kalman filter
potential fields
sensor-based planning
trajectory prediction
uncertainty
Predictive and Multi-rate Sensor-Based Planning under Uncertainty
info:eu-repo/semantics/article
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