Field Insights for Portable Vine Robots in Urban Search and Rescue

Soft, growing vine robots are well-suited for exploring cluttered, unknown environments, and are theorized to be performant during structural collapse incidents caused by earthquakes, fires, explosions, and material flaws. These vine robots grow from the tip, enabling them to navigate rubble-filled passageways easily. State-of-the-art vine robots have been tested in archaeological and other field settings, but their translational capabilities to urban search and rescue (USAR) are not well understood. To this end, we present a set of experiments designed to test the limits of a vine robot system, the Soft Pathfinding Robotic Observation Unit (SPROUT), operating in an engineered collapsed structure. Our testing is driven by a taxonomy of difficulty derived from the challenges USAR crews face navigating void spaces and their associated hazards. Initial experiments explore the viability of the vine robot form factor, both ideal and implemented, as well as the control and sensorization of the system. A secondary set of experiments applies domain-specific design improvements to increase the portability and reliability of the system. SPROUT can grow through tight apertures, around corners, and into void spaces, but requires additional development in sensorization to improve control and situational awareness.

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Publication Details

Published
2024-11-12
DOI
https://doi.org/10.1109/ssrr62954.2024.10770046
Citations
4
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
1.00
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article

Field Insights for Portable Vine Robots in Urban Search and Rescue

Margaret M. Coad, Nathaniel Hanson, Ciera McFarland, Ankush Dhawan et al.
4 citations
Robotic Path Planning Algorithms
1.00
article

Field Insights for Portable Vine Robots in Urban Search and Rescue

Margaret M. Coad, Nathaniel Hanson, Ciera McFarland, Ankush Dhawan, Riya Kumari, Chad Council
article en
4 citations

Abstract

Soft, growing vine robots are well-suited for exploring cluttered, unknown environments, and are theorized to be performant during structural collapse incidents caused by earthquakes, fires, explosions, and material flaws. These vine robots grow from the tip, enabling them to navigate rubble-filled passageways easily. State-of-the-art vine robots have been tested in archaeological and other field settings, but their translational capabilities to urban search and rescue (USAR) are not well understood. To this end, we present a set of experiments designed to test the limits of a vine robot system, the Soft Pathfinding Robotic Observation Unit (SPROUT), operating in an engineered collapsed structure. Our testing is driven by a taxonomy of difficulty derived from the challenges USAR crews face navigating void spaces and their associated hazards. Initial experiments explore the viability of the vine robot form factor, both ideal and implemented, as well as the control and sensorization of the system. A secondary set of experiments applies domain-specific design improvements to increase the portability and reliability of the system. SPROUT can grow through tight apertures, around corners, and into void spaces, but requires additional development in sensorization to improve control and situational awareness.

University of Notre Dame (US), MIT Lincoln Laboratory (US), Massachusetts Institute of Technology (US)
Sustainable cities and communities
Openalex Percentile: Top 22%
Robotic Path Planning Algorithms
1.00
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