Learning Inverse Kinodynamics for Autonomous Vehicle Drifting
M. Suvarna O. Tehrani
In this work, we explore a data-driven learning-based approach to learning
the kinodynamic model of a small autonomous vehicle, and observe the effect it
has on motion planning, specifically autonomous drifting. When executing a
motion plan in the real world, there are numerous causes for error, and what is
planned is often not what is executed on the actual car. Learning a kinodynamic
planner based off of inertial measurements and executed commands can help us
learn the world state. In our case, we look towards the realm of drifting; it
is a complex maneuver that requires a smooth enough surface, high enough speed,
and a drastic change in velocity. We attempt to learn the kinodynamic model for
these drifting maneuvers, and attempt to tighten the slip of the car. Our
approach is able to learn a kinodynamic model for high-speed circular
navigation, and is able to avoid obstacles on an autonomous drift at high speed
by correcting an executed curvature for loose drifts. We seek to adjust our
kinodynamic model for success in tighter drifts in future work.