Lee, Seung Hun (University of Michigan), Jo, Wonse (University of Michigan), Robert, Lionel (University of Michigan Ann Arbor), Tilbury, Dawn M. (Univ of Michigan)

Local Minima Prediction using Dynamic Bayesian Filtering for UGV Navigation in Unstructured Environments

Scheduled for presentation during the Regular Session "Advanced Motion Planning and Navigation" (FrBT2), Friday, July 18, 2025, 14:20−14:40, Room 106

Joint 10th IFAC Symposium on Mechatronic Systems and 14th Symposium on Robotics, July 15-18, 2025, Paris, France

This information is tentative and subject to change. Compiled on August 2, 2025

Abstract

Path planning is crucial for the navigation of autonomous vehicles, yet these vehicles face challenges in complex and real-world environments. Although a global view may be provided, it is often outdated, necessitating the reliance of Unmanned Ground Vehicles (UGVs) on real-time local information. This reliance on partial information, without considering the global context, can lead to UGVs getting stuck in local minima. This paper develops a method to proactively predict local minima using Dynamic Bayesian filtering, based on the detected obstacles in the local view and the global goal. This approach aims to enhance the autonomous navigation of self-driving vehicles by allowing them to predict potential pitfalls before they get stuck, and either ask for help from a human, or re-plan an alternate trajectory.