Robust sampling-based trajectory tracking for autonomous vehicles

In real world motion planning tasks, autonomous vehicles can easily deviate away from their planned trajectories due to external disturbances, uncertain wheel/leg-terrain interaction, and other errors in the model used for planning. A possible solution to this problem consists in the continuous usage of replanning strategies. However, replanning is in general computationally intensive and its use should be minimized when possible. In this paper, a new methodology for robust trajectory tracking is proposed. The method generates, via sampling, correcting control inputs to drive the vehicle back to the desired trajectory. Due to the use of sampling, the methodology easily incorporates nonlinear planning models and integrates seamlessly with sampling-based motion planners. The paper presents simulation and preliminary experimental results showing the efficacy of the proposed approach and thus its potential application to motion planning tasks with real-time constraints.