Safe Corridor Learning For Model Predictive Path Following Control

Safe collision-free operation of autonomous systems, such as mobile robots in crowded, uncertain, only partially known environments, is challenging. We propose learning a collision-free corridor from demonstration via heteroscedastic Gaussian processes. We incorporate available deterministic obstacle information in the learning procedure to derive safety guarantees for the corridor. The learned passage is utilized in a model predictive path planning controller that steers the system safely through the environment. The achievable results are underlined in simulations considering a mobile robot. Copyright (C) 2022 The Authors.