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Advanced Structures & Materials

Annual Plan

Adaptive Structures with Embedded Autonomy for Advancing Ground Vehicles

Project Team

Principal Investigator

Kon-Well Wang, University of Michigan


Matthew Castanier, US Army GVSC


Ellen C. Lee, Ford Motor Company

Jayanth Kudva, NextGen Aeronautics, Inc.


Patrick Dorin, University of Michigan

Project Summary

Project begins 2023.

Mechanical metamaterials and metastructures have become emerging building blocks for advanced engineering systems, exhibiting significant advantages over conventional materials. With the rapid advances in high-performance systems, such as autonomous vehicles, we are recently witnessing a prominent demand for metastructures to have much more built-in intelligence and autonomy. These structural systems must effectively respond to mission and environmental changes, where they would observe, learn, make decisions, and execute actions in a highly coordinated manner. Achieving these intelligent elements in a distributed complex system would be inevitably cumbersome and inefficient if one entirely relies on the conventional platform built solely on add-on digital electronics and computers. Therefore, studies have emerged to include intelligence more directly in the mechanical domain, the so-called mechano-intelligence. While the concept is promising, the current efforts are preliminary and ad-hoc. That is, there is lack of a systematic foundation for designing, embedding, and integrating the different elements of mechano-intelligence, such as observation, learning, memorizing, decision-making and execution, in complex metastructures.

The vision of this proposed basic research is to create a paradigm shift in uncovering metastructures that can physically observe, think, decide and act. Our research goal is to advance the state of art by developing new knowledge and the needed foundation mentioned above to pioneer a new metastructure concept via harnessing the framework of physical computing (computing in the mechanical domain) concurrently with observation, learning, memorizing, decision-making and execution so to create, embed, and integrate these elements of mechano-intelligence. Surpassing the traditional approaches with add-on electronics and digital computer, this proposed research would uncover knowledge to create systems that are more energy-efficient, faster in reaction, and much more durable in harsh environments. Moreover, we are paving a novel path to add new functions and autonomy to vehicle structures without burdening the onboard computers, and thus without the concern of cybersecurity.

A key to this project’s success is to derive innovations to create, embed, and integrate the different elements of mechano-intelligence, which leads to our basic research questions:

Question 1: How to derive functional-relevant elements to advance from physical computing to integrated mechano- intelligence?

Question 2: What are the correlations between mechanical systems’ properties and their mechano-intelligent performance?

Question 3: How should the intelligence in the mechanical domain interface with electronics efficiently while maximizing performance?