Kinematics-Only Differential Flatness Based Trajectory Tracking for Autonomous Racing

In autonomous racing, accurately tracking the race line at the limits of handling is essential to guarantee competitiveness. In this study, we show the effectiveness of Differential Flatness based control for high-speed trajectory tracking for car-like robots. We compare the tracking performance of our controller against Nonlinear Model Predictive Control and resource use while running on embedded hardware and show that on average KFC reduces the computation resource usage by 50 % while performing on par with NMPC. Our implementation of the proposed controller, the simulation environment and detailed results is open-sourced on https://github.com/droneslab/.