UP3: Unsupervised Predictive Path Planner for Mobile Robots in Unknown Environment

Abstract

In this article, UP3, a novel unsupervised learning‐based predictive path planning framework for mobile robot navigation in partially observable environments is proposed. An offline unsupervised learning method is used for planner training to avoid frequent environment interactions or expert demonstration. The network employs attention‐based path optimization to generate the predictive path. Herein, the robot's safety is considered as a hard constraint, and the control barrier function constraint is used to design the collision loss. Additionally, a deep constraint correction module is designed to correct the predictive path, which aims to make the path satisfy the constraints. Herein, the approach through simulation and real‐world experiments is tested to verify the function of each module. In the results, it is indicated that UP3 performs well on dense and maze scenarios and has good generalization.