Rick Johnson, an engineering professor on the Hill—and, at the risk of a mixed metaphor, something of a Renaissance man. At Cornell since 1981, Johnson has spent decades teaching and doing research in electrical engineering, particularly in the fields of control systems and signal processing. But over the past twelve years, his interests have entailed as much art as science. A pioneer in the field of computational art history, Johnson leverages both his engineering acumen and his abiding passion for art to study the physical materials with which works are made. Read more about Magic Eye
Soft Robot Design
A Cornell team of researchers—Robert Shepherd and Hadas Kress-Gazit, Mechanical and Aerospace Engineering, and Amit Lal, Electrical and Computer Engineering—are advancing a new class of motors for soft robots. These motors are based on the electrically driven pumping of liquid. Called HASELs (hydraulically amplified self-healing electrostatic actuators), they have the potential to enable practical and useful soft robots for safe interactions with humans.
HASELs represent the first electrostatic soft actuators that are stable and useful enough for broad adoption by experts and non-experts alike.
The researchers are providing a framework for the widespread adoption of HASELs by addressing their manufacturing, power, and control in easily replicated motifs that will be made available to the public in free online databases. Robot components such as hands and legs will be designed and manufactured using 3D printing. Effective 3D printing designs and control software developed in the project will be made available for public download and use.
Engineering design principles and useful tools that will allow citizen scientists to proliferate useful robots in the future will be an important outcome of the project. In order to achieve these goals, the project will leverage the opportunity for a coordinated, multidisciplinary effort between experts in physics, materials science, microelectronic engineering, as well as low- and high-level controls.
Original article by Cornell Research
Image credit (graphic): Beatrice Jin
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