Multi-Objective Design Optimization of a Soft, Pneumatic Robot
Design Optimization, Mobile Robots, Soft Robots
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).
BYU ScholarsArchive Citation
Killpack, Marc D.; Bodily, Daniel M.; and Allen, Thomas F., "Multi-Objective Design Optimization of a Soft, Pneumatic Robot" (2017). Faculty Publications. 3225.
IEEE International Conference on Robotics and Automation (ICRA)
Ira A. Fulton College of Engineering and Technology
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