We present a data driven approach to construct a library of feedback motion primitives for non-holonomic vehicles that guarantees bounded error in following arbitrarily long trajectories. This ensures that motion re-planning can be avoided as long as disturbances to the vehicle remain within a certain bound and also potentially when the obstacles are displaced within a certain bound. The library is constructed along local abstractions of the dynamics that enables addition of new motion primitives through abstraction refinement. We provide sufficient conditions for construction of such robust motion primitives for a large class of nonlinear dynamics, including commonly used models, such as the standard Reeds-Shepp model. The algorithm is applied for motion planning and control of a rover with slipping without its prior modelling.
We show that under some assumptions on vehicle dynamics and environment uncertainty it is possible to automatically synthesize motion primitives that do not accumulate error over time.