Efficient kinodynamic trajectory generation for wheeled robots

Planning dynamic motion is computationally demanding and thus can hardly be done in real-time onboard robots. In this paper, we present an analytic approximation to predict the dynamic state of wheeled robots with non-holonomic constraints, given a start state and a sequence of piecewise constant controls. Our approximations are accurate and fast to calculate. They can be used to replace numerical integrators in kinodynamic planning algorithms. The predictions are differentiable and allow us to utilize gradient descent methods to solve the inverse dynamics as well and generate trajectories connecting arbitrary points in state space.