Creating robots comprised of highly deformable components is a relatively new area of study. These soft robots can potentially deform through spaces smaller than their original cross section, traverse varying landscapes, and be manufactured with relative ease when compared to rigid platforms. Such varied potential capabilities make soft robots extremely valuable for missions involving covert access, disaster recovery, and biomedical applications. However, little previous work has been done on creating soft robots able to produce dependable locomotion.
Due to a lack of previous work related to crawling soft robots, new tools are needed to enable open-loop control schemes and soft robot morphologies that result in crawling. Although much work has been done with rigid snake-like robots designed for similar applications, lessons learned can only sometimes be applied to soft robotic platforms. When translating rigid mechanics principles onto soft hardware, the added degrees of freedom, axial compressibility, and complex non-linear dynamics of soft robot structures must be considered.
New ways to create efficient soft robot crawling have been investigated in this thesis. With the use of a dynamic simulation utility, lumped dynamic modeling, and genetic algorithms, robot morphologies and control commands were optimized for soft robot performance. The validity of this methodology was verified on a pneumatically actuated soft robot, and it was shown that optimized control commands translated to reality with relative success. It was also demonstrated that lumped dynamic modeling can be a valid method when evolving and optimizing soft robot control parameters. Finally, the speed at which optimal results were reached was further increased with physics-based pre-processing. This allows more genetic algorithm generations to occur and new models to be evolved in less time. The validity and speed of the methods described in this thesis allow for reliable evolution of crawling soft robot morphologies and control parameters in a workable time frame.
|Commitee:||Leisk, Gary, Trimmer, Barry, White, Robert|
|School Location:||United States -- Massachusetts|
|Source:||MAI 47/05M, Masters Abstracts International|
|Subjects:||Mechanical engineering, Robotics|
|Keywords:||Biomimetics, Genetic algorithm, Optimization, Soft robots|
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