Enhancing UGV Path Planning using Dynamic Bayesian Filtering to Predict Local Minima

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 Although a global view may be provided, it is likely to be outdated, necessitating the reliance of Unmanned Ground Vehicles (UGVs) on real-time local information.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. Obstacles beyond the known or locally-sensed areas can result in inaccurate predictions of local minima. This thesis focuses on proactively predicting these 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.