LAUSR

We propose an efficient and safe dynamic window approach (DWA) by using deterministic sampling. When the system dynamics have uncertainty, the control input includes errors, so that the DWA objective function becomes a random variable. When a random-choice algorithm with a finite number of samples is used to estimate the objective function, it may miss collisions during prediction. In this work, we approximate the end-state distribution as a one-dimensional distribution for each input candidate in advance and generate sample paths deterministically to eliminate the misses to achieve safe control. Numerical experiments have shown that this method is approximately three times as efficient as the Monte Carlo method in most indoor environments.