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Power-efficient adaptive behavior through a shape-changing elastic robot
Adaptive Behavior ( IF 1.6 ) Pub Date : 2021-03-05 , DOI: 10.1177/1059712321990186
Shiv Ashutosh Katiyar 1 , Darwin Gouwanda 1 , Fumiya Iida 2 , Surya Girinatha Nurzaman 1
Affiliation  

The adaptive morphology of a robot, such as shape adaptation, plays a significant role in adapting its behaviors. Shape adaptation should ideally be achieved without considerable cost, like the power required to deform the robot’s body, and therefore, it is reasonably considered as the last resort in classical rigid robots. However, the last decade has seen an increasing interest in soft robots: robots that can achieve deformability through their inherent material properties or structural compliance. Nevertheless, the dynamics of these types of robots is often complex and therefore it is difficult to substantiate whether the cost like the required power for changing its shape will be worthwhile to achieve the desired behavior. This article presents an approach in the development and analysis of a shape-changing locomoting robot, which relies on the ability of elastic beams to deform and vibrate. Through a proper use of elastic materials and the robot’s vibration-based dynamics, it will be shown both analytically and experimentally how shape adaptation can be designed such that it leads to desirable behaviors, with better power efficiency compared to when the robot solely relies on changing its control input. The results encourage emerging direction in robotics that investigates approaches to change robots’ behaviors through their adaptive morphology.



中文翻译:

通过变形的弹性机器人实现节电的自适应行为

机器人的自适应形态(例如形状适应)在适应其行为方面起着重要作用。理想情况下,形状调整应在不花费大量成本的前提下实现,例如使机器人的身体变形所需的动力,因此,合理地将其视为经典刚性机器人的最后手段。然而,在过去的十年中,人们对软机器人的兴趣日益增长:可以通过其固有的材料特性或结构柔韧性实现变形能力的机器人。然而,这些类型的机器人的动力学通常很复杂,因此很难证实是否需要像改变其形状所需的动力这样的成本来实现所需的性能。本文提出了一种开发和分析变形机器人的方法,这取决于弹性梁的变形和振动能力。通过适当地使用弹性材料和机器人的基于振动的动力学,将在分析和实验上展示如何设计形状适应性,以使其产生理想的行为,与机器人仅依靠改变时相比,具有更高的功率效率。它的控制输入。结果鼓励了机器人技术的新兴方向,该方向研究了通过适应性形态改变机器人行为的方法。与机器人仅依靠更改其控制输入时相比,具有更高的电源效率。结果鼓励了机器人技术的新兴方向,该方向研究了通过适应性形态改变机器人行为的方法。与机器人仅依靠更改其控制输入时相比,具有更高的电源效率。结果鼓励了机器人技术的新兴方向,该方向研究了通过适应性形态改变机器人行为的方法。

更新日期:2021-03-07
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