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Automatic Configuration Recognition Methods in Modular Robots

机译:模块化机器人中的自动配置识别方法

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摘要

Recognizing useful modular robot configurations composed of hundreds of modules is a significant challenge. Matching a new modular robot configuration to a library of known configurations is essential in identifying and applying control schemes. We present three different algorithms to address the problem of (a) matching and (b) mapping new robot configurations onto a library of known configurations. The first method solves the problem using graph isomorphisms and can identify configurations that share the same underlying graph structure, but have different port connections amongst the modules. The second approach compares graph spectra of configuration matrices to find a permutation matrix that maps a given configuration to a known one. The third algorithm exploits the unique structure of the problem for the particular robots used in our research to achieve impressive gains in performance and speed over existing techniques, especially for larger configurations. With these three algorithms, this paper presents novel solutions to the problem of configuration recognition and sheds light on theoretical and practical issues for long-term advances in this important area of modular robotics. Results and examples are provided to compare the performance of the three algorithms and discuss their relative advantages.
机译:识别由数百个模块组成的有用的模块化机器人配置是一项重大挑战。将新的模块化机器人配置与已知配置的库进行匹配对于识别和应用控制方案至关重要。我们提出了三种不同的算法来解决(a)匹配和(b)将新的机器人配置映射到已知配置的库中的问题。第一种方法使用图形同构来解决该问题,并且可以识别共享相同基础图形结构但模块之间具有不同端口连接的配置。第二种方法比较配置矩阵的图谱,以找到将给定配置映射到已知配置的置换矩阵。第三种算法利用了我们研究中使用的特定机器人的问题的独特结构,从而在性能和速度上都超过了现有技术,特别是对于大型配置而言,取得了令人印象深刻的收益。借助这三种算法,本文为配置识别问题提供了新颖的解决方案,并为模块化机器人技术这一重要领域的长期发展提供了理论和实践问题。提供结果和示例以比较这三种算法的性能并讨论它们的相对优势。

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