Learning Inverse Kinodynamics for Autonomous Vehicle Drifting
This work addresses motion planning errors for autonomous vehicles in complex drifting maneuvers, though it is incremental as it focuses on specific high-speed scenarios and leaves tighter drifts for future work.
The paper tackles the problem of autonomous vehicle drifting by learning a data-driven kinodynamic model from inertial measurements and executed commands, enabling high-speed circular navigation and obstacle avoidance by correcting curvature for loose drifts.
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.