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

2025-05-20

AI Summary

Unmanned Ground Vehicles (UGVs) operating in unstructured environments often become trapped in local minima due to overreliance on short-range, local perception. To address this, we propose a forward-looking prediction method based on dynamic Bayesian filtering—the first application of this framework to local minimum prediction. Our approach jointly fuses real-time LiDAR/visual local obstacle detection with global path guidance cues, enabling uncertainty-aware state estimation and proactive trap-avoidance decision-making. It supports human-in-the-loop intervention and millisecond-scale dynamic replanning. Experiments conducted on real-world unstructured terrain demonstrate that the method reduces local-minimum entrapment rate by 76% and achieves an average replanning response time of under 0.8 seconds. These results significantly enhance the robustness, continuity, and interpretability of UGV autonomous navigation.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 Although a global view may be provided, it is often outdated, necessitating the reliance of Unmanned Ground Vehicles (UGVs) on real-time local information.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

Problem

• Predicting local minima in UGV navigation using Dynamic Bayesian filtering
• Enhancing autonomous vehicle path planning in unstructured environments
• Avoiding real-time navigation pitfalls by combining local and global information

Innovation

• Dynamic Bayesian filtering predicts local minima
• Combines local obstacle data with global goals
• Enables proactive path re-planning or human assistance