Evolutionary robotics offers a powerful framework for designing and evolving robot morphologies, particularly in the context of modular robots. However, the role of query mechanisms during the genotype-to-phenotype mapping process has been largely overlooked. This research addresses this gap by conducting a comparative analysis of query mechanisms in the brain-body co-evolution of modular robots. Using two different query mechanisms, Breadth-First Search (BFS) and Random Query, within the context of evolving robot morphologies using CPPNs and robot controllers using tensors, and testing them in two evolutionary frameworks, Lamarckian and Darwinian systems, this study investigates their influence on evolutionary outcomes and performance. The findings demonstrate the impact of the two query mechanisms on the evolution and performance of modular robot bodies, including morphological intelligence, diversity, and morphological traits. This study suggests that BFS is both more effective and efficient in producing highly performing robots. It also reveals that initially, robot diversity was higher with BFS compared to Random Query, but in the Lamarckian system, it declines faster, converging to superior designs, while in the Darwinian system, BFS led to higher end-process diversity.

翻译：进化机器人学为设计和进化机器人形态提供了强大框架，尤其在模块化机器人领域。然而，基因型到表型映射过程中查询机制的作用在很大程度上被忽略了。本研究通过对比分析模块化机器人脑-体协同进化中的查询机制来填补这一空白。采用两种不同的查询机制——广度优先搜索（BFS）与随机查询——在利用CPPN进化机器人形态及使用张量作为机器人控制器的基础上，将其置于拉马克与达尔文两种进化框架中进行测试，探究了它们对进化结果和性能的影响。实验结果表明，这两种查询机制对模块化机器人身体的进化与性能（包括形态智能、多样性及形态特征）具有显著影响。本研究表明，BFS在生成高性能机器人方面既更有效也更高效。同时发现，在初始阶段，BFS相比随机查询产生的机器人多样性更高，但在拉马克系统中其多样性下降更快，并收敛于更优设计；而在达尔文系统中，BFS则带来了更高的末期多样性。