Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
复制标题
多个混沌中心模式发生器，具有腿部运动学习和故障补偿功能
DOI：
10.1016/j.ins.2014.05.001
复制
发表时间：
2015-02-10
影响因子：
8.1
通讯作者：
Manoonpong, Poramate
中科院分区：
文献类型：
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作者：
Ren, Guanjiao;Chen, Weihai;Manoonpong, Poramate
一个原本混沌的系统可以被控制成各种周期动力学。当它被实现到腿式机器人的运动控制作为中央模式发生器（CPG），复杂的步态模式出现，使机器人可以执行各种步行行为。然而，这样一个单一的混沌CPG控制器有困难处理腿故障。具体来说，在这里介绍的场景中，它的运动永久地偏离了期望的轨迹。为了解决这个问题，我们扩展了单一的混沌CPG多CPG的学习。学习机制是基于模拟退火算法。在正常情况下，CPG同步并且它们的动态是相同的。在腿部功能障碍或残疾的情况下，CPG失去同步，导致独立动力学。在这种情况下，学习机制被应用于自动调整其余腿的振荡频率，使得机器人适应其运动来处理故障。因此，由多个混沌CPG产生的轨迹比仅由单个CPG产生的轨迹更接近原始轨迹。该系统的性能进行评估，首先在物理模拟的四足以及六足机器人，最后在一个真实的六条腿的步行机称为AMOSII。这里提出的实验结果表明，使用多个CPG与学习是自适应运动生成的有效方法，例如，不同的身体部位必须执行独立的运动故障补偿。(C)2014 Elsevier Inc. All rights reserved.
An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation. (C) 2014 Elsevier Inc. All rights reserved.