Visual Exploration-Enhanced Quadruped Robot with Active-Passive Composite Telescope Mechanism
Navigating hazardous environments and confined spaces during disaster search and rescue operations poses significant challenges to both human rescuers and conventional robotic systems. Recognizing the need for enhanced capabilities, we introduce a Visual Exploration-Enhanced Quadruped Robot (VEQR), a novel platform specifically designed to improve adaptability and exploration in such demanding scenarios. The VEQR combines a quadruped robot with an innovative active-passive composite telescopic mechanism—a compact, 12.8 mm diameter device equipped with a camera—that enables maneuvering through narrow and curved spaces like slits and debris-filled areas. The bending and insertion/retraction capabilities of this mechanism improve the dexterity of the robot in confined spaces and extend its reach into deeper, hard-to-access zones. Experimental results demonstrate the VEQR’s proficiency in overcoming obstacles, inspecting confined spaces, and providing clear visual feedback. Notably, the VEQR successfully traversed narrow and winding passages as small as 50 mm diameter and inspected targets positioned up to 0.6 mm deep within confined spaces, confirming its effectiveness as a robust robot for search and rescue operations in hazardous environments.
Authors
- Jianshu Zhou (ORCID: https://orcid.org/0000-0003-3900-3519)
- Jiajun An (ORCID: https://orcid.org/0000-0002-8624-7076)
- Shengzhi Wang (ORCID: https://orcid.org/0000-0002-6705-6792)
- Weibing Li (ORCID: https://orcid.org/0000-0002-7970-9076)
- Xin Ma (ORCID: https://orcid.org/0000-0002-7058-0165)
- Puchen Zhu (ORCID: https://orcid.org/0000-0003-3446-4535)
- Xuchen Wang (ORCID: https://orcid.org/0000-0002-0119-3268)
- Yiying Liang
Institutions
- Sun Yat-sen University (CN)
- Chinese University of Hong Kong (HK)
- Purdue University West Lafayette (US)
- University Town of Shenzhen (CN)
- University of California, Berkeley (US)
- Tsinghua University (CN)
Publication Details
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1109/irac63143.2024.10871418
- Primary Topic
- Modular Robots and Swarm Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Chinese University of Hong Kong
- Shun Hing Institute of Advanced Engineering