Genetic-algorithm Seeding of Idiotypic Networks for Mobile-robot Navigation
Genetic-algorithm Seeding of Idiotypic Networks for Mobile-robot Navigation
复制标题
用于移动机器人导航的独特型网络的遗传算法播种
DOI：
10.2139/ssrn.2831226
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
J. Garibaldi
中科院分区：
文献类型：
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作者：
Amanda M. Whitbrook;U. Aickelin;J. Garibaldi
机器人控制设计者已经开始利用人类免疫系统的特性，
产生动态系统，可以适应复杂的，变化的，现实世界的任务。Jerne的独特型网络理论已被证明是最流行的人工免疫系统（AIS）方法纳入基于行为的机器人，因为独特型选择产生高度适应性的反应。然而，以前的努力主要集中在发展网络连接，并且通常使用单一的预先设计的行为集，限制可变性。本文描述了一种方法，编码的行为作为一个可变的属性集，并表明，当编码与遗传算法（GA），多套不同的行为可以自然和迅速地发展，提供更大的范围灵活的行为选择。该算法进行了广泛的测试与模拟电子冰球机器人，导航周围的迷宫跟踪颜色。结果表明，非常成功的行为集可以在大约25分钟内生成，并且当使用多个自治种群时，可以获得更大的多样性，而不是单个种群。
Robot-control designers have begun to exploit the properties of the human immune system in order to
produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.