通过演化与学习结合，设计出性能更优的非常规六旋翼无人机。
Unconventional Hexacopters via Evolution and Learning: Performance Gains and New Insights
- 让无人机形态和控制策略共同演化，实现自适应优化。
- 新设计的无人机在复杂任务中显著超越传统六旋翼机型。
- 方法可推广至其他具身智能系统，适合机器人与进化计算研究者。
演化与学习长期相互关联，近年来其协同作用受到更多关注。当前新趋势是形态演化——即具身人工智能系统（如机器人）物理结构的演化。本文研究具有可演化形态与可学习控制器的六旋翼无人机系统，在空中机器人领域，展示了演化与学习结合可生成非传统构型的无人机，并在比以往文献更复杂的任务中显著优于传统六旋翼机。在进化计算领域，提出新颖评估指标并揭示形态演化与学习之间此前未被发现的交互效应。分析工具具备领域无关性，为整合演化与学习的具身智能系统构建了方法论基础。
原文摘要 · Abstract (English)
Evolution and learning have historically been interrelated topics, and their interplay is attracting increased interest lately. The emerging new factor in this trend is morphological evolution, the evolution of physical forms within embodied AI systems such as robots. In this study, we investigate a system of hexacopter-type drones with evolvable morphologies and learnable controllers and make contributions to two fields. For aerial robotics, we demonstrate that the combination of evolution and learning can deliver non-conventional drones that significantly outperform the traditional hexacopter on several tasks that are more complex than previously considered in the literature. For the field of Evolutionary Computing, we introduce novel metrics and perform new analyses into the interaction of morphological evolution and learning, uncovering hitherto unidentified effects. Our analysis tools are domain-agnostic, making a methodological contribution towards building solid foundations for embodied AI systems that integrate evolution and learning.
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