VL-Explore: Zero-shot Vision-Language Exploration and Target Discovery by Mobile Robots
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_ | 1866915405321207808 |
|---|---|
| author | Zhang, Yuxuan Abdullah, Adnan Koppal, Sanjeev J. Islam, Md Jahidul |
| author_facet | Zhang, Yuxuan Abdullah, Adnan Koppal, Sanjeev J. Islam, Md Jahidul |
| contents | Vision-language navigation (VLN) has emerged as a promising paradigm, enabling mobile robots to perform zero-shot inference and execute tasks without specific pre-programming. However, current systems often separate map exploration and path planning, with exploration relying on inefficient algorithms due to limited (partially observed) environmental information. In this paper, we present a novel navigation pipeline named "VL-Explore" for simultaneous exploration and target discovery in unknown environments, leveraging the capabilities of a vision-language model named CLIP. Our approach requires only monocular vision and operates without any prior map or knowledge about the target. For comprehensive evaluations, we designed a functional prototype of a UGV (unmanned ground vehicle) system named "Open Rover", a customized platform for general-purpose VLN tasks. We integrated and deployed the VL-Explore pipeline on Open Rover to evaluate its throughput, obstacle avoidance capability, and trajectory performance across various real-world scenarios. Experimental results demonstrate that VL-Explore consistently outperforms traditional map-traversal algorithms and achieves performance comparable to path-planning methods that depend on prior map and target knowledge. Notably, VL-Explore offers real-time active navigation without requiring pre-captured candidate images or pre-built node graphs, addressing key limitations of existing VLN pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08791 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VL-Explore: Zero-shot Vision-Language Exploration and Target Discovery by Mobile Robots Zhang, Yuxuan Abdullah, Adnan Koppal, Sanjeev J. Islam, Md Jahidul Robotics Vision-language navigation (VLN) has emerged as a promising paradigm, enabling mobile robots to perform zero-shot inference and execute tasks without specific pre-programming. However, current systems often separate map exploration and path planning, with exploration relying on inefficient algorithms due to limited (partially observed) environmental information. In this paper, we present a novel navigation pipeline named "VL-Explore" for simultaneous exploration and target discovery in unknown environments, leveraging the capabilities of a vision-language model named CLIP. Our approach requires only monocular vision and operates without any prior map or knowledge about the target. For comprehensive evaluations, we designed a functional prototype of a UGV (unmanned ground vehicle) system named "Open Rover", a customized platform for general-purpose VLN tasks. We integrated and deployed the VL-Explore pipeline on Open Rover to evaluate its throughput, obstacle avoidance capability, and trajectory performance across various real-world scenarios. Experimental results demonstrate that VL-Explore consistently outperforms traditional map-traversal algorithms and achieves performance comparable to path-planning methods that depend on prior map and target knowledge. Notably, VL-Explore offers real-time active navigation without requiring pre-captured candidate images or pre-built node graphs, addressing key limitations of existing VLN pipelines. |
| title | VL-Explore: Zero-shot Vision-Language Exploration and Target Discovery by Mobile Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2502.08791 |