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

TL;DR

This paper introduces a method using Dynamic Bayesian filtering to predict local minima in UGV navigation, improving real-time obstacle avoidance and trajectory planning in unstructured environments.

Contribution

It presents a novel approach for proactive local minima prediction in UGV navigation using Dynamic Bayesian filtering based on local obstacle detection and global goals.

Findings

Enhanced prediction accuracy of local minima

Improved navigation success rate in complex environments

Reduced instances of UGVs getting stuck

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 Although a global view may be provided, it is often outdated, necessitating the reliance of Unmanned Ground Vehicles (UGVs) on real-time local information.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.