移动机器人主动嗅觉气源定位方法研究综述

doi:10.3778/j.issn.1002-8331.2410-0128

摘要/Abstract

摘要： 随着能源化工产业的发展，有毒有害气体泄漏事故日益受到公众关注，应用机器人主动嗅觉技术实现泄漏气体溯源与定位已成为研究热点之一。目前，大多数研究综述是针对某一类方法在气味源定位中的应用进行总结，未能全面地总结机器人主动嗅觉技术的发展历程与研究进展。重点对移动机器人主动嗅觉气源定位方法展开综述，针对气源定位中烟羽发现、烟羽跟踪、气源确认三大子任务，详细对经典算法、研究现状与存在的问题进行分析。最后对机器人气源定位技术未来的发展趋势进行了讨论，针对当前机器人主动嗅觉研究中环境适应性不足、真实场景鲁棒性较差等问题，提出了多感官融合、烟羽跟踪算法优化及复杂环境应用等方面的研究展望，为下一步移动机器人主动嗅觉气源定位研究提供了一定的思路。

关键词: 机器人嗅觉, 气源定位, 主动嗅觉, 移动机器人, 信息融合

Abstract: The incidents of toxic and hazardous gas leakage have increasingly come to the attention of the public with the rapid expansion of the energy chemical industry. The application of robotic active olfaction technology has emerged as a hot research topic with the objective of achieving the ability to trace and localize gas leakage spots. At present, most research reviews focus on the application of a specific approach to odor source localization, lacking a comprehensive overview of the development history and current progress of robotic active olfactory technology. This paper provides an overview of active olfactory odor source localization methods for mobile robots, details the classical algorithms, the current status of research in this field, and the challenges inherent to the three subtasks: plume discovery, plume tracking, and odor source confirmation. The paper concludes with a discussion of the prospective evolution of robot odor source localization technology. In addressing issues such as lack of environmental adaptability and poor real-world robustness, the paper puts forward research directions including multi-sensory fusion, plume tracking algorithm optimization, and applications in complex environments, and provides insights for future mobile robot olfactory research.

Key words: robot olfaction, odor source localization, active olfaction, mobile robots, information fusion

何丽, 杜洋鋆, 李知远, 冉腾, 肖文东, 姚佳程. 移动机器人主动嗅觉气源定位方法研究综述[J]. 计算机工程与应用, 2025, 61(10): 36-49.

HE Li, DU Yangjun, LI Zhiyuan, RAN Teng, XIAO Wendong, YAO Jiacheng. Review of Active Olfactory Odor Source Localization Methods for Mobile Robots[J]. Computer Engineering and Applications, 2025, 61(10): 36-49.

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