Adaptive and Energy Efficient Walking in a Hexapod Robot Under Neuromechanical Control and Sensorimotor Learning
Adaptive and Energy Efficient Walking in a Hexapod Robot Under Neuromechanical Control and Sensorimotor Learning
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DOI：
10.1109/tcyb.2015.2479237
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发表时间：
2016-11
影响因子：
11.8
通讯作者：
Xiaofeng Xiong;F. Wörgötter;P. Manoonpong
中科院分区：
文献类型：
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作者：
Xiaofeng Xiong;F. Wörgötter;P. Manoonpong
多足动物行走的控制是一个神经机械过程，以自适应和节能的方式实现这一点是一个困难和具有挑战性的问题。这是由于该过程需要在真实的时间内：1）协调关节腿的非常多的自由度; 2）产生适当的腿刚度（即，顺应性）;以及3）确定在腿的端点处产生特定位置的关节角度。为了解决这个问题的机器人应用程序，在这里，我们提出了一个神经机械控制器加上感觉运动学习。该控制器由一个模块化的神经网络，用于协调18个关节和几个虚拟的激动剂拮抗剂肌肉机制（VAAM）的可变顺应性关节运动。此外，包括前向模型和双速率学习过程的感觉运动学习，被引入用于预测脚力反馈和用于在线调整VAAM的刚度参数。控制和学习机制，使六足机器人先进的移动传感器驱动的步行设备（AMOS），以实现可变的顺应性步行，适应不同的步态和表面。因此，与其他小型腿机器人相比，AMOS可以执行更节能的行走。此外，本文还表明，神经控制与可调肌肉样功能的紧密结合，在感觉反馈的指导下，再加上感觉运动学习，是一种更好地理解和解决多足运动中自适应协调问题的方法。
The control of multilegged animal walking is a neuromechanical process, and to achieve this in an adaptive and energy efficient way is a difficult and challenging problem. This is due to the fact that this process needs in real time: 1) to coordinate very many degrees of freedom of jointed legs; 2) to generate the proper leg stiffness (i.e., compliance); and 3) to determine joint angles that give rise to particular positions at the endpoints of the legs. To tackle this problem for a robotic application, here we present a neuromechanical controller coupled with sensorimotor learning. The controller consists of a modular neural network for coordinating 18 joints and several virtual agonist-antagonist muscle mechanisms (VAAMs) for variable compliant joint motions. In addition, sensorimotor learning, including forward models and dual-rate learning processes, is introduced for predicting foot force feedback and for online tuning the VAAMs' stiffness parameters. The control and learning mechanisms enable the hexapod robot advanced mobility sensor driven-walking device (AMOS) to achieve variable compliant walking that accommodates different gaits and surfaces. As a consequence, AMOS can perform more energy efficient walking, compared to other small legged robots. In addition, this paper also shows that the tight combination of neural control with tunable muscle-like functions, guided by sensory feedback and coupled with sensorimotor learning, is a way forward to better understand and solve adaptive coordination problems in multilegged locomotion.