Local Minima Prediction using Dynamic Bayesian Filtering for UGV Navigation in Unstructured Environments
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910957493551104 |
|---|---|
| author | Lee, Seung Hun Jo, Wonse Robert Jr., Lionel P. Tilbury, Dawn M. |
| author_facet | Lee, Seung Hun Jo, Wonse Robert Jr., Lionel P. Tilbury, Dawn M. |
| contents | 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 Although a global view may be provided, it is often outdated, necessitating the reliance of Unmanned Ground Vehicles (UGVs) on real-time local information.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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14337 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Local Minima Prediction using Dynamic Bayesian Filtering for UGV Navigation in Unstructured Environments Lee, Seung Hun Jo, Wonse Robert Jr., Lionel P. Tilbury, Dawn M. Robotics 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 Although a global view may be provided, it is often outdated, necessitating the reliance of Unmanned Ground Vehicles (UGVs) on real-time local information.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. |
| title | Local Minima Prediction using Dynamic Bayesian Filtering for UGV Navigation in Unstructured Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.14337 |