This Faculty Early Career Development (CAREER) grant will study the physical principles underlying the behavior of a new type of soft robot: the tip-growing plant-inspired “vine robot.” Currently, the formal understanding for this new soft robot is limited. This project will investigate the fundamental limits and full range of capabilities of the vine robots. The benefits of this project are threefold: (1) Progress of Science - The basic understanding of vine robots created during this work will enable the design of robots as tools for scientific discovery. For instance, vine robots will aid archeologists exploring ruins, and biologists exploring underground creatures; (2) Societal - The robots enabled by this work will demonstrate new functionality; miniaturized medical vine robots will enable safe and efficient endovascular surgery to advance national health, and burrowing vine robots will enable access to the largely unexplored subterranean world; and (3) Educational - The soft robot-centered educational content, designed to be engaging and active, while eliciting creativity, problem solving and critical thinking, will prepare the next generation of engineers and scientists to make impactful contributions to the world.

The research objective of this project is to use analytical modeling and hypothesis-driven experimentation to elucidate the physical principles governing the behavior of vine robots. It is critical to create the body of knowledge that rigorously describes its behavior to realize the full potential of this concept. This project will be accomplished in three Aims, in which the researcher will: (1) Investigate dynamic growth in free space to establish the underlying physical principles; (2) Investigate dynamic growth in constrained environments: and (3) Apply principles from Aims 1 and 2 to enable new vine robot applications: medical vine robots in the body’s pathways and burrowing robots in the subterranean world. The knowledge gained—from new scaling laws to a formalization of vine robot workspace to a predictive understanding of obstacle interaction force—will help lay the foundational understanding for this fledgling sub-field of soft robotics. This will enable not only parameter optimization, but fundamentally new approaches and designs for vine robots. More broadly, the new understanding will advance the field of soft robotics—advancing our understanding of soft systems that deform and dynamically adapt to their environment.

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.

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University of California Santa Barbara
Santa Barbara
United States
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