Workshop on Biodiversity and Bioinspiration

Abstract

The central idea behind bioinspired engineering is that biological systems can provide insights that lead to novel engineering conce pts and solutions. A fundamental reason why biological systems continue to outperform human designs is the nature of the evolutionar y optimization process that has operated on vastly longer time scales and over vastly larger number of prototypes (i.e., individua ls of different species) than would be possible in any engineering effort. Its immense scale has given biological evolution the abil ity to find solutions to high-dimensional optimization problems that continue to elude engineers.The large performance gaps that rem ain between engineering and biology, especially in areas that require multidimensional solutions, make biological model systems attr active to engineers. At the same time, the ever-increasing ability of engineers to analyze and synthesize complex systems is placing replicating biological solutions within the reach of engineering. As a result, bioinspiration has already been highly successful in a variety of engineering areas, e.g., materials and surfaces, sensors and actuators, as well as control systems engineering.Realizi ng the full potential of bioinspired engineering will require the development of new scientific fundamentals that will enable mining engineering concepts from comparisons across extensive sets of biological species. Accomplishing this could turn bioinspiration fro m a craft into a mature engineering discipline that could be used to mine the worlds biodiversity as a natural resource for enginee ring innovation. Current developments in different areas of science and engineering are converging in ways that could make achieving this goal possible. More effective and detailed ways of digitizing biological form and function, e.g., specimen digitization and mo tion capture, can deliver large amounts of quantitative/digital information across large number of species. Methods for computationa l analysis, e.g., for fluid dynamics, allow it to make predictions of the physical effects associated with biological form and funct ion. Ongoing advances in the data sciences will make it possible to extract insight from the large bodies of digital data on biologi cal shape, dynamics, and the associated physical effects. Finally, the ever-improving capabilities of engineering to create complex systems will allow engineers to create hardware (and software) paradigms to embody these insights.The proposed workshop will seek to introduce a biodiversity perspective to bioinspired engineering solutions. We will cast the net widely without restricting ourselve s to single applications or modalities. To do so, the workshop will come up with a road map for the development of a scientific appr oach to harnessing information gathered across multiple biological species. Biology essentially provides endless stimulating illustr ations of time-tested designs made freely available to us. Three characteristics of biology andbioinspiration will be considered dur ing the workshop: Function, Simplicity, and Flexibility. These three characteristics can be applied, for example, to bioinspired eng ineering of robots. Given the current widespread interest in robotics and the expertise of several workshop organizers in this resea rch area, the workshop is likely to revolve around such topics. It is expected that a comparative analysis of the integrative approa ch that animals take to sensing and locomotion in their habitats could provide critical insights towards solving the problem of achi eving autonomous mobility in structure-rich natural environments.

Document Details

Document Type
DoD Grant Award
Publication Date
Oct 22, 2021
Source ID
N629092112064

Entities

People

  • Ulmar Grafe

Organizations

  • Office of Naval Research
  • United States Navy

Tags

Readers

  • Mycotoxin ecology in Amazonian ecosystems.
  • Research Science/Academic Research
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Biotechnology