Skip to main navigation Skip to search Skip to main content

I-Corps: Translation Potential of Miniaturized Solid-State Actuators for Inspection Robots

Project: Research

Abstract & Details

Description

Award ID: 2528227

This I-Corps project is based on the development of a robot for use in inspecting complex machinery, such as jet engines. The inspection robot market is rapidly expanding, driven by a growing need for efficient, accurate, and cost-effective inspection solutions in sectors such as aviation, manufacturing, oil and gas, infrastructure, and energy. These robots play a crucial role by detecting faults, corrosion, or structural issues that could lead to costly failures if left unchecked. However, traditional inspection robots often face limitations when inspecting hard-to-reach or confined spaces, such as narrow pipelines, small machinery components, or tight structural cavities. This challenge has accelerated the demand for miniaturized robots designed to operate in these restricted environments. Conventional approaches use tendon-driven mechanisms, which are limited by friction that reduces range of motion along tortuous paths. This technology is an electrically driven system that bypasses the limitations of friction in tortuous environments. The solution addresses these challenges at a smaller scale using miniaturization to reach previously inaccessible points to detect failure points earlier in the process. This miniaturization may allow for faster and easier inspections of complex machinery. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of solid-state, miniaturized dielectric elastomer actuator (DEAs) inspection robots. This dielectric elastomer actuator navigation technology overcomes the challenge of tendon driven systems, which are limited by friction. Navigation using miniaturized complementary metal-oxide-semiconductor (CMOS) sensors allows access to narrow points, beyond the limit of conventional borescopes. In addition, the strategy for building solid state DEAs and connecting them with small metallic wires enables multi-degree-of-freedom end effectors. The result is a solid-state inspection tool that does not have friction limitations and can carry smaller cameras than existing borescopes. This technology may have application in inspection of complex industrial components such as jet engines, exploration of unstructured environments such as earthquake damaged buildings, or medical applications such as a steerable catheter for navigation inside the body. This technology may improve the inspection process by improving the speed of performing inspections as well as the ability to reach previously inaccessible points. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman
StatusActive
Effective start/end date06/15/2508/31/26

Funding

  • I-Corps Teams: $50,000.00

Active Fiscal Year

  • FY2026
  • FY2025

Start Fiscal Year

  • FY2025

TIP Programs

  • I-Corps Teams

Key Technology Areas

  • Advanced Computing and Semiconductors
  • (confidence score: 99%)
  • Robotics and Advanced Manufacturing
  • (confidence score: 100%)

Technology Foci

  • Advanced Manufacturing (excluding biomanufacturing and semiconductor manufacturing)
  • (confidence score: 93%)
  • Automation
  • (confidence score: 88%)
  • Robotics
  • (confidence score: 100%)
  • Advanced Computing and Semiconductors (Broad)
  • (confidence score: 100%)

Congressional District at Award

  • District n. 02 of Connecticut

Current Congressional District

  • District n. 02 of Connecticut

United States

  • Connecticut

Core Based Statistical Area (CBSA)

  • Hartford-West Hartford-East Hartford, CT

County

  • County: Capitol, CT

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint. Learn more about Elsevier's Fingerprint Engine here: https://beta.elsevier.com/products/elsevier-fingerprint-engine