论文标题
模块化机器人中的集中和分散控制及其对形态的影响
Centralized and Decentralized Control in Modular Robots and Their Effect on Morphology
论文作者
论文摘要
在进化机器人技术中，进化算法用于将形态学和控制配置。但是，合作化会带来不同的挑战：您如何优化一个经常改变其输入和输出数量的机构的控制器？然后，研究人员必须在集中或分散的控制之间做出一些选择。在本文中，我们研究了集中式和分散控制器对模块化机器人性能和形态的影响。这是通过实施一个集中式和两个分散的连续时间复发性神经网络控制器以及基线的正弦波控制器来完成的。我们发现，与形态大小更独立的分散方法的表现明显优于其他方法。它在各种形态大小中也很好地工作。此外，我们强调了为不断变化的形态实施集中控制的困难，并认为我们的集中式控制器在早期融合中比其他方法更挣扎。我们的发现表明，重复的分散网络在发展模块化机器人的形态和控制时是有益的。总体而言，如果这些发现转化为其他机器人系统，那么我们所遇到的结果和问题可以帮助未来的研究人员在合作地形态和控制时选择控制方法。
In Evolutionary Robotics, evolutionary algorithms are used to co-optimize morphology and control. However, co-optimizing leads to different challenges: How do you optimize a controller for a body that often changes its number of inputs and outputs? Researchers must then make some choice between centralized or decentralized control. In this article, we study the effects of centralized and decentralized controllers on modular robot performance and morphologies. This is done by implementing one centralized and two decentralized continuous time recurrent neural network controllers, as well as a sine wave controller for a baseline. We found that a decentralized approach that was more independent of morphology size performed significantly better than the other approaches. It also worked well in a larger variety of morphology sizes. In addition, we highlighted the difficulties of implementing centralized control for a changing morphology, and saw that our centralized controller struggled more with early convergence than the other approaches. Our findings indicate that duplicated decentralized networks are beneficial when evolving both the morphology and control of modular robots. Overall, if these findings translate to other robot systems, our results and issues encountered can help future researchers make a choice of control method when co-optimizing morphology and control.
