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.
翻译:在进化机器人学中,进化算法被用来对形态学和控制进行共同优化。然而,协同优化导致不同的挑战:如何优化一个经常改变投入和产出数量的机构的控制器?研究人员随后必须在集中或分散控制之间做出某些选择。在本篇文章中,我们研究了中央和分散控制器对模块机器人性能和形态学的影响。我们通过实施一个集中和两个分散的连续连续时连续神经网络控制器以及一个基准线的正弦波控制器来完成这项工作。我们发现,一种更独立于形态学规模的分散方法的表现比其他方法要好得多。同样,它还在更多种多样的形态学规模上运作良好。此外,我们着重指出了对变化形态学实施集中控制的困难,并看到我们的中央控制器与其他方法相比,早期的趋同力更挣扎。我们的调查结果表明,在模块机器人的形态学和控制演变过程中,重复的分散网络是有益的。总体而言,如果这些发现转化为其他机器人系统,我们的结果和遇到的问题可以帮助未来的研究人员在共同控制时选择一种方法。