Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding
Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding
复制标题
模拟模块化机器人的进化和形态发生：直接编码和生成编码之间的比较
DOI：
10.1007/978-3-319-55849-3_56
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
K. Støy
中科院分区：
文献类型：
--
作者：
Frank Veenstra;A. Faíña;S. Risi;K. Støy
模块化机器人在进化机器人技术中提供了一个重要的好处，即快速评估现实中进化的形态和控制系统。然而，模拟模块化机器人的人工进化是一个困难和耗时的任务，需要大量的计算能力。虽然虚拟生物中的人工进化利用了强大的生成编码，但在这里，我们研究了当机器人模块的数量发生变化时，生成编码和直接编码如何比较模块化机器人中运动的进化。模拟更少的模块将减小直接编码的基因组的大小，而实现的生成编码的基因组的大小保持不变。我们发现，当模拟最多5个、10个和20个模块时，生成编码在进化的初始阶段创建机器人表型的效率要高得多。这不仅证实了生成式编码可以更快地获得良好的性能，而且在模拟几个模块时，生成式编码比直接编码更强大，可以创建机器人结构。经过更长的进化时间，编码之间的差异不再具有统计学意义。这使我们推测，一种组合的方法-从生成编码开始，然后实现直接编码-可以导致更有效的进化设计。
Modular robots offer an important benefit in evolutionary robotics, which is to quickly evaluate evolved morphologies and control systems in reality. However, artificial evolution of simulated modular robotics is a difficult and time consuming task requiring significant computational power. While artificial evolution in virtual creatures has made use of powerful generative encodings, here we investigate how a generative encoding and direct encoding compare for the evolution of locomotion in modular robots when the number of robotic modules changes. Simulating less modules would decrease the size of the genome of a direct encoding while the size of the genome of the implemented generative encoding stays the same. We found that the generative encoding is significantly more efficient in creating robot phenotypes in the initial stages of evolution when simulating a maximum of 5, 10, and 20 modules. This not only confirms that generative encodings lead to decent performance more quickly, but also that when simulating just a few modules a generative encoding is more powerful than a direct encoding for creating robotic structures. Over longer evolutionary time, the difference between the encodings no longer becomes statistically significant. This leads us to speculate that a combined approach – starting with a generative encoding and later implementing a direct encoding – can lead to more efficient evolved designs.