弹性六足机器人

2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT) Pub Date : 2017-10-01 DOI:10.1109/ICAT.2017.8171613

D. Trivun, H. Dindo, B. Lacevic

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引用次数: 5

摘要

在本文中，我们提出了一种学习特定机器人系统期望行为的方法，并在系统局部故障的情况下转移现有知识。采用在Bioloid平台上搭建的六足机器人(hexapod)进行方法验证。我们使用遗传算法来优化六足机器人的步态，然后我们模拟对机器人造成的物理损伤。该方法的目标是根据机器人的实际形态而不是假设的形态来优化步态。此外，以前获得的知识将被转移，以改善结果。该算法采用特定混合种群的非标准遗传算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Resilient hexapod robot

In this paper, we present a method of learning desired behaviour of the specific robotic system and transfer of the existing knowledge in the event of partial system failure. Six-legged robot (hexapod) built on top of the Bioloid platform is used for the method verification. We use genetic algorithms to optimize the hexapod's gait, after which we simulate physical damage caused to the robot. The goal of this method is to optimize the gait in accordance with the actual robot morphology, instead of the assumed one. Also, knowledge that was previously gained will be transferred in order to improve the results. Nonstandard genetic algorithm with the specific mixed population is used for this.

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2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT)

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