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

Seung Hun Lee, Wonse Jo, Lionel P. Robert, Dawn M. Tilbury

TL;DR

The paper tackles local minima in APF-based UGV navigation within unstructured environments by proposing a Dynamic Bayesian Filtering framework to predict potential minima using local obstacle observations and the global goal. It defines Local Minimum $X_{lm}$ and sensory areas (AOI, RA, UA), and uses a three-step DBF process—Prediction with state transitions $P(X_{lm}^t \mid X_{lm}^{t-1}, u_t)$, Correction with observation likelihood $P(z_t \mid X_{lm}^t)$, and Normalization—to update a belief $bel_t(X_{lm})$ until a threshold $\gamma$ signals possible stalling. The approach demonstrates earlier, high-confidence predictions of local minima compared with two existing methods in two scenarios, enabling proactive replanning or human intervention. This work advances robust UGV navigation under partial observability by turning local-minima risk into an actionable, probabilistic forecast with known remaining steps. Practically, it supports safer, more reliable operation of autonomous ground vehicles in off-road, unstructured settings.

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 Although a global view may be provided, it is often outdated, necessitating the reliance of Unmanned Ground Vehicles (UGVs) on real-time local information.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

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

TL;DR

The paper tackles local minima in APF-based UGV navigation within unstructured environments by proposing a Dynamic Bayesian Filtering framework to predict potential minima using local obstacle observations and the global goal. It defines Local Minimum and sensory areas (AOI, RA, UA), and uses a three-step DBF process—Prediction with state transitions , Correction with observation likelihood , and Normalization—to update a belief until a threshold signals possible stalling. The approach demonstrates earlier, high-confidence predictions of local minima compared with two existing methods in two scenarios, enabling proactive replanning or human intervention. This work advances robust UGV navigation under partial observability by turning local-minima risk into an actionable, probabilistic forecast with known remaining steps. Practically, it supports safer, more reliable operation of autonomous ground vehicles in off-road, unstructured settings.

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.

Paper Structure

This paper contains 17 sections, 4 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Backgrounds and Related Works
  3. Artificial Potential Field (APF)
  4. Discrete Time Control
  5. Local Minima Prediction Methods
  6. Dynamic Bayesian Filtering-based Local Minima Prediction Algorithm
  7. Local Minimum Prediction
  8. Dynamic Bayesian Filtering
  9. Prediction Step
  10. Correction Step
  11. Normalization
  12. Simulation Test and Results
  13. Simulation Setup
  14. Comparison Methods
  15. Results
  16. ...and 2 more sections

Figures (6)

  • Figure 1: A bird's eye view for UGV navigation using Artificial Potential Field (APF).
  • Figure 2: Examples of AOI, RA($X_{t_0}$), and UA($X_{t_0}$) used in the proposed local minima prediction algorithm.
  • Figure 3: Occupied points angle in (a) Case 1 - Wall and (b) Case 2 - Hallway.
  • Figure 4: Two occupancy simulation environments: (a) Case 1 - Wall and (b) Case 2 - Hallway.
  • Figure 5: Two comparison methods: (a) Method I and (b) Method II.
  • ...and 1 more figures

Theorems & Definitions (5)

  • Definition 1: Local Minimum ($X_{lm}$)
  • Definition 2: Sensing Area (SA(X))
  • Definition 3: Area Definitions
  • Definition 4: State Transition Probability
  • Definition 5: Observation Likelihood