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A method for co-evolving morphology and walking pattern of biped humanoid robot

机译:一种混炼形态和行走模式的方法和行走模式的弯曲人体机器人

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摘要

In this paper, we present a method for coevolving structures and controller of biped walking robots. Currently, biped walking humanoid robots are designed manually on trial-and-error basis. Although certain control theory exists, such as zero moment point (ZMP) compensation, these theories assume humanoid robot morphology is given in advance. Thus, engineers have to design control program for apriori designed morphology. If morphology and locomotion are considered simultaneously, we do not have to spare time with trial-and-error. Therefore a method useful for designing the robot is proposed At first, the simple models of both morphology and controller are used for the dynamic simulation, which are multi-link model as morphology and two kinds of controllers. One is a layered neural network and the other is neural oscillator. The robots with the optimal energy efficiency of walking are designed with Genetic Algorithm. As a result, various combinations of morphologies and gaits are generated, and obtained relationship between length of each link and moving distance which gives the optimal energy efficiency. Moreover, the robots are encoded from limited size of chromosomes.
机译:在本文中,我们介绍了一种用于耦合鞋面行走机器人的结构和控制器的方法。目前,双方行走人形机器人在试验和错误的基础上手动设计。尽管存在某些控制理论,例如零时刻点(ZMP)补偿,但这些理论提前给出人形机器人形态。因此,工程师必须为Apriori设计的形态设计控制程序。如果同时考虑形态和运动,我们不必使用试验和错误的业余时间。因此,首先提出了一种用于设计机器人的方法,操作形态和控制器的简单模型用于动态仿真,它们是多链路模型作为形态和两种控制器。一个是层状神经网络,另一个是神经振荡器。具有遗传算法的最佳能量效率的机器人设计。结果,生成了各种形态和Gaits的组合,并获得了每个链路长度与移动距离之间的关系,其提供了最佳能量效率。此外,机器人从有限的染色体尺寸编码。

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