利用机器人平台定位飞蛾激发的气味源:全面回顾

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kumar Gaurav, Prabhat Ranjan
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引用次数: 0

摘要

任何气味线索都可以追踪到它的释放源。所谓的“来源定位”已经在动物的许多重要任务中被观察到,包括寻找食物或配偶。特别是,科学界长期以来一直致力于解开飞蛾的复杂行为,同时追求远距离雌性飞蛾释放的性信息素。这些研究提供了许多见解,包括飞行路线,感觉器官和信息素处理的细节。反过来,这些知识为工程师和研究人员提供了灵感,以设计寻源算法,而感觉器官/机制则导致了昆虫-机器混合系统。因此,本文主要围绕后两种方法进行综述:(1)在机器人平台上实现受飞蛾启发的算法;(2)使用生物传感器,如天线或昆虫-机器混合系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Moth-inspired odor source localization using robotic platforms: A comprehensive review
Any odor cue can be traced to find its release source. So-called “source localization” has been observed in animals in many important tasks including finding food or mates. In particular, the scientific community for a long time focused on unraveling the complex behavior of moths while in pursuit of sex pheromones emitted by their distant female counterpart. These studies have provided many insights including details of the flight paths, sensory organs, and pheromone processing. In turn, this knowledge has provided inspiration to engineers and researchers to devise source-seeking algorithms, whereas sensory organs/-mechanisms led to insect-machine hybrid systems. Therefore, this review revolves around these last two approaches specifically (1) the implementation of moth-inspired algorithms in robotic platforms and the (2) use of biosensors such as antennae or insect-machine hybrid systems.
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来源期刊
Adaptive Behavior
Adaptive Behavior 工程技术-计算机:人工智能
CiteScore
4.30
自引率
18.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: _Adaptive Behavior_ publishes articles on adaptive behaviour in living organisms and autonomous artificial systems. The official journal of the _International Society of Adaptive Behavior_, _Adaptive Behavior_, addresses topics such as perception and motor control, embodied cognition, learning and evolution, neural mechanisms, artificial intelligence, behavioral sequences, motivation and emotion, characterization of environments, decision making, collective and social behavior, navigation, foraging, communication and signalling. Print ISSN: 1059-7123
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