A Data‐Driven Review of Soft Robotics

Abstract

The past decade of soft robotics has delivered impactful and promising contributions to society and has seen exponentially increasing interest from scientists and engineers. This interest has resulted in growth of the number of researchers participating in the field and the quantity of their resulting contributions, stressing the community's ability to comprehend and build upon the literature. In this work, a data‐driven review is presented that addresses the recent surge of research by providing a quantitative snapshot of the field. Relevant data are catalogued with three levels of analysis. First, publication‐level analysis explores high‐level trends in the field and bibliometric relationships across the more detailed analyses. Second, device‐level analysis examines the tethering of robots and the incorporation of component types (actuators, sensors, controllers, power sources) into each robot. Finally, component‐level analysis investigates the compliances, material compositions, and “function media” (energetic methods by which components operate) of each soft robotic component in the analyzed literature. The reported data indicate a significant reliance on elastomeric materials, electrical and fluidic media, and physical tethering; meanwhile, controllers and power sources remain underdeveloped relative to actuators and sensors. These gaps in the surveyed literature are elaborated upon, and promising future directions for the field of soft robotics are identified.

Document Details

Document Type
Pub Defense Publication
Publication Date
Dec 07, 2021
Source ID
10.1002/aisy.202100163

Entities

People

  • Barclay Jumet
  • Daniel J Preston
  • Marquise D. Bell
  • Vanessa Sanchez

Organizations

  • Harvard University
  • National Aeronautics and Space Administration
  • National Science Foundation
  • Rice University
  • United States Department of Defense
  • Wyss Institute for Biologically Inspired Engineering

Tags

Readers

  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy