Abstract
Background: As research on quadruped robots grows, so does the variety of designs available. These designs are often inspired by nature and finalized around various technical, instrumentation-based constraints. However, no systematic methodology of kinematic parameter selection to reach performance specifications is reported so far. Kinematic design optimization with objective functions derived from performance metrics in dynamic tasks is an underexplored, yet promising area. Methods: This article proposes to use genetic algorithms to handle the designing process. Given the dynamic tasks of jumping and trotting, body and leg link dimensions are optimized. The performance of a design in genetic algorithm search iterations is evaluated via full-dynamics simulations of the task. Results: The article presents comparisons of design results optimized for jumping and trotting separately. Significant dimensional dissimilarities and associated performance differences are observed in this comparison. A combined performance measure for jumping and trotting tasks is studied too. It is discussed how significantly various structural lengths affect dynamic performances in these tasks. Results are compared to a relatively more conventional quadruped design too. Conclusions: The task-specific nature of this optimization process improves the performances dramatically. This is a significant advantage of the systematic kinematic parameter optimization over straight mimicking of nature in quadruped designs. The performance improvements obtained by the genetic algorithm optimization with dynamic performance indices indicate that the proposed approach can find application area in the design process of a variety of robots with dynamic tasks.
Highlights
The application of genetic algorithms (GAs) is reported in the field of structural optimization where the problem is geometrical and topological design.[1,2] The challenge of designing efficient proportions for individual elements in a complex structure is a multilevel optimization task that has been tackled with GAs.[3,4] It is used in optimizing the kinematic arrangement of mechanisms with links and joints[5] and in optimization of robotic manipulator designs.[6,7] These robotic applications are limited to fixed-based manipulators, where the aim is to improve the workspace of the end effector
Its results can be used as a means of comparison for our current research of kinematic arrangement optimization presented in this article
While kinematic designs including robot body and leg link lengths are inspired from nature, there is the lack of a systematic approach for the selection of these parameters
Summary
The application of genetic algorithms (GAs) is reported in the field of structural optimization where the problem is geometrical and topological design.[1,2] The challenge of designing efficient proportions for individual elements in a complex structure is a multilevel optimization task that has been tackled with GAs.[3,4] It is used in optimizing the kinematic arrangement of mechanisms with links and joints[5] and in optimization of robotic manipulator designs.[6,7] These robotic applications are limited to fixed-based manipulators, where the aim is to improve the workspace of the end effector. The optimization is carried out within a certain scale range and with defined available power via full dynamic simulations This quadruped robot is planned to be used as a research platform for experimenting on tasks such as walking and running while mostly focusing on jumping. Conclusions: The task-specific nature of this optimization process improves the performances dramatically. This is a significant advantage of the systematic kinematic parameter optimization over straight mimicking of nature in quadruped designs. The performance improvements obtained by the genetic algorithm optimization with dynamic performance indices indicate that the proposed approach can find application area in the design process of a variety of robots with dynamic tasks
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