Feedback motion planning for car-like robots

Abstract

In this study, we address the problem of feedback motion planning for a car-like robot de-scribed by a kinematic model, navigating through obstacles. For this purpose, we designed two sampling-based algorithms equipped with the feedback property, a Modifie dRapidly Exploring Random Tree Star (RRT*) algorithm and a Funnel-Graph algorithm. Both al-gorithms generate collision-free paths while accounting for non-holonomic constraints and uncertainties. These generated paths are then fed to the pure pursuit controller which han-dles the robot motion execution. Our approach is validated by testing the algorithms’ ability to handle differen tuncertaintie san dadaptability to environmental changes,including a performance comparison between them. The results show the superiority of the funnel-graph algorithm across all tests, which makes it best suited for real-time applications.