Multi-Objective Design Optimization of a Soft, Pneumatic Robot

Keywords

Design Optimization, Mobile Robots, Soft Robots

Abstract

We present a method for the design optimization of a soft, inflatable robot. The method described utilizes a multi-objective fitness function together with custom, platform-specific metrics related to the dexterity and load-bearing capacity of inflatable manipulators. Candidate designs are scored by computing these metrics at many randomly generated configurations and then by appropriately combining these scores within the multi-objective optimization framework. High performing designs are propagated through a genetic algorithm. The final result is a set of diverse, optimal designs lying along a Pareto front spanning the design space. By examining variations and trade-offs within this set, a designer can more appropriately choose design parameters for a target application. This is especially relevant for robots with many design parameters that can quickly be manufactured as is the case with emerging, soft robot technologies.

Original Publication Citation

Daniel M. Bodily, Thomas F. Allen, and Marc D. Killpack, "Multi-objective design optimization of a soft, pneumatic robot," 2017 IEEE International Conference on Robotics and Automation (ICRA), 1864-1871 (2017).

Document Type

Peer-Reviewed Article

Publication Date

2017-07-24

Permanent URL

http://hdl.lib.byu.edu/1877/6036

Publisher

IEEE International Conference on Robotics and Automation (ICRA)

Language

English

College

Ira A. Fulton College of Engineering and Technology

Department

Mechanical Engineering

University Standing at Time of Publication

Full Professor

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