Bellegarda, GuillaumeNguyen, Quan2022-01-312022-01-312022-01-312022-01-0110.1109/LCSYS.2021.3136142https://infoscience.epfl.ch/handle/20.500.14299/184900WOS:000736739300004In this letter we present a versatile trajectory optimization framework that leverages a fused kinematic-dynamic bicycle model for highly dynamic vehicle drifting maneuvers. Our framework can be used online to generate drifting maneuvers, offline to plan drift parking, and additionally enables online tracking of the offline computed parking maneuvers. Importantly, neither individual kinematic nor dynamic bicycle models alone can be used straightforwardly in an optimization framework to plan nor execute the presented motions, as the former cannot model drifting, and the latter becomes ill-defined at low speeds. We validate our framework in a Gazebo simulation of the MIT RACECAR, where we show several drifting scenarios such as steady-state drifting with a range of desired yaw rates as well as a dynamic drift parking maneuver under noisy conditions, and video results can be found at https://youtu.be/MF1_fS6CQQs.Automation & Control Systemsvehicle dynamicsbicycleskinematicstireswheelssteady-statemathematical modelsnonlinear model predictive controlvehicle drift controlautonomous driftingautonomous vehiclestext::journal::journal article::research article