用于二维软机器人执行器生成设计的中间编码层:CPPN、L系统和随机生成的比较

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Mathematical & Computational Applications Pub Date : 2023-05-15 DOI:10.3390/mca28030068
M. Venter, Naudé Thomas Conradie
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引用次数: 0

摘要

介绍了二维软机器人单元生成设计中三种明确定义的中间编码方法的比较方法。本研究评估了一种传统的遗传算法,该算法使用隐式随机编码层、模拟生物生长模式的林登迈尔系统编码和用于生成2D模式的合成模式生成网络编码,完全可以从设计领域中删除元素。优化问题的目标是在内部压力下将单个致动器单元的变形与期望的目标形状(特别是单轴伸长)相匹配。研究结果表明,与传统隐式编码遗传算法相比,Lindenmayer系统编码产生的候选单元函数求值更少。然而,约束和内能的分布与随机编码相似,并且Lindenmayer系统编码产生的候选单元的总体多样性较小。相比之下,尽管比Lindenmayer系统编码需要更多的功能评估,但组合模式生成网络编码产生了相似的候选单元多样性。总的来说,组合模式生成网络编码比随机或Lindenmayer系统编码产生更多的高性能单元,使其成为传统单片方法的可行替代方案。结果表明,产生网络编码的组合模式可能是设计具有理想性能特性的软机器人执行器的一种有前途的方法。
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Intermediate Encoding Layers for the Generative Design of 2D Soft Robot Actuators: A Comparison of CPPN’s, L-Systems and Random Generation
This paper introduced a comparison method for three explicitly defined intermediate encoding methods in generative design for two-dimensional soft robotic units. This study evaluates a conventional genetic algorithm with full access to removing elements from the design domain using an implicit random encoding layer, a Lindenmayer system encoding mimicking biological growth patterns and a compositional pattern producing network encoding for 2D pattern generation. The objective of the optimisation problem is to match the deformation of a single actuator unit with a desired target shape, specifically uni-axial elongation, under internal pressure. The study results suggest that the Lindenmayer system encoding generates candidate units with fewer function evaluations than the traditional implicitly encoded genetic algorithm. However, the distribution of constraint and internal energy is similar to that of the random encoding, and the Lindenmayer system encoding produces a less diverse population of candidate units. In contrast, despite requiring more function evaluations than the Lindenmayer System encoding, the Compositional Pattern Producing Network encoding produces a similar diversity of candidate units. Overall, the Compositional Pattern Producing Network encoding results in a proportionally higher number of high-performing units than the random or Lindenmayer system encoding, making it a viable alternative to a conventional monolithic approach. The results suggest that the compositional pattern producing network encoding may be a promising approach for designing soft robotic actuators with desirable performance characteristics.
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来源期刊
Mathematical & Computational Applications
Mathematical & Computational Applications MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
自引率
10.50%
发文量
86
审稿时长
12 weeks
期刊介绍: Mathematical and Computational Applications (MCA) is devoted to original research in the field of engineering, natural sciences or social sciences where mathematical and/or computational techniques are necessary for solving specific problems. The aim of the journal is to provide a medium by which a wide range of experience can be exchanged among researchers from diverse fields such as engineering (electrical, mechanical, civil, industrial, aeronautical, nuclear etc.), natural sciences (physics, mathematics, chemistry, biology etc.) or social sciences (administrative sciences, economics, political sciences etc.). The papers may be theoretical where mathematics is used in a nontrivial way or computational or combination of both. Each paper submitted will be reviewed and only papers of highest quality that contain original ideas and research will be published. Papers containing only experimental techniques and abstract mathematics without any sign of application are discouraged.
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