Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines.
Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines.
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DOI：
10.3389/fncir.2013.00012
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发表时间：
2013
影响因子：
3.5
通讯作者：
Wörgötter F
中科院分区：
文献类型：
--
作者：
Manoonpong P;Parlitz U;Wörgötter F
生物，比如会走路的动物，已经找到了解决运动控制问题的迷人方法。它们的运动表现出优雅的印象，包括多功能，节能和适应性运动。在过去的几十年里，机器人专家试图通过使用不同的方法，包括机器学习算法，经典的工程控制技术和生物启发的控制机制，用人工腿运动系统来模仿这种自然特性。然而，它们的性能水平与自然的性能水平仍然相差甚远。相比之下，动物的运动机制似乎在很大程度上不仅取决于中央机制（中央模式发生器，CPG）和感觉反馈（基于传入的控制），而且还取决于内部前向模型（传出副本）。它们在不同的动物中有不同程度的使用。一般来说，CPG组织由感觉反馈形成的基本节奏运动，而内部模型用于感觉预测和状态估计。根据这一概念，我们在这里提出的自适应神经运动控制的CPG机制与神经调节和局部腿控制机制的基础上的感觉反馈和自适应神经前向模型与传出副本。这种神经闭环控制器使步行机器能够执行多种不同的步行模式，包括昆虫般的腿部运动和步态以及节能运动。此外，前向模型允许机器自主调整其运动，以应对地形变化，在站立阶段失去地面接触，在摆动阶段踩到或碰到障碍物，腿部损伤，甚至促进蟑螂般的攀爬行为。因此，这里提出的结果表明，所采用的体现神经闭环系统可以是一个强大的方式来开发强大的和适应性强的机器。
Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.
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影响因子：
4.3
作者：
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通讯作者：
Leblebicioglu, Kemal
影响因子：
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作者：
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DOI：
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发表时间：
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影响因子：
2.1
作者：
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通讯作者：
Cruse, H
DOI：
10.1098/rsta.2006.1912
发表时间：
2007-01-15
影响因子：
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作者：
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通讯作者：
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