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Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling be- tween components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball shaped tensegrity robot. Experimental results per- formed with a variety of evolutionary algorithms in a de- tailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400\% better than a hand-coded solution, while the multiagent evolution performs 800\% better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.
@inproceedings{tumer-iscen_gecco13,
author = {A. Iscen and A. Agogino and V. SunSpiral and K. Tumer},
title = {Controlling Tensegrity Robots through Evolution},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
month = {July},
address = {Amsterdam, The Netherlands},
abstract={Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling be- tween components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball shaped tensegrity robot. Experimental results per- formed with a variety of evolutionary algorithms in a de- tailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400\% better than a hand-coded solution, while the multiagent evolution performs 800\% better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.},
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Robotics, Evolutionary Algorithms},
year = {2013}
}
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