动态气流环境中机器人主动嗅觉鲁棒寻源方法研究

项目名称： 动态气流环境中机器人主动嗅觉鲁棒寻源方法研究

项目编号： No.60875053

项目类型： 面上项目

立项/批准年度： 2009

项目学科： 金属学与金属工艺

项目作者： 孟庆浩

作者单位： 天津大学

项目金额： 29万元

中文摘要： 嗅觉在生物世界已被广泛地应用。受到生物嗅觉的启发，自二十世纪九十年代开始，一些学者开始使用移动机器人结合气味/气体传感器实现气味源定位、气味分布地图构建和气味轨迹引导等功能，即所谓的机器人主动嗅觉。主动嗅觉的研究涉及移动机器人导航与控制、传感及信息处理﹑仿生学﹑计算智能和流体力学等多个研究领域。它在有毒/有害气体泄漏源查找、违禁品检查、灾难营救及防生化恐怖袭击等诸多方面均有着潜在的应用前景。 针对湍流主控气态流体环境下的气味源定位问题的研究，本课题取得的创新性研究成果包括： 第一、提出了气味包路径的概念及气味包路径估计方法，给出了一套气味源确认方案，其中包括基于粒子滤波的气味源位置估计算法和基于统计方法的气味源识别规则。第二，提出了用于多机器人气味源定位的算法，包括改进蚁群优化结合逆风搜索算法以及概率粒子群优化算法；基于气味源的持久性和发散性特点，结合启发式思想和气味质量通量计算，提出了三步气味源确认算法。第三，提出了基于最小二乘估计和包容体系结构的机器人视/嗅觉融合方法并用于气体泄漏源搜寻，提高了气体源定位的可靠性。 以上所提算法的可行性均通过计算机仿真及真实机器人实验进行了验证。

中文关键词： 主动嗅觉；湍流主控环境；气味包路径估计；多机器人；嗅视觉融合

英文摘要： The sense of smell has been widely used in biological world. Inspired by the biological olfaction, in the early 1990s researchers started to build mobile robots with onboard gas/odor sensors achieving the so-called active olfaction, which include the functions such as odor source localization, odor distribution mapping and odor path guidance. The research on active olfaction is related to the research fields such as mobile robot navigation and control, sensing and information processing, bionics, computation intelligence and hydrodynamics. It is expected that robot active olfaction will play more and more roles in the application areas like judging toxic/harmful gas leakage location, checking contraband, searching for survivors in collapsed buildings and fighting against terrorist. Aiming at the odor source localization in turbulence dominated airflow environments, the novel achievements can be concluded as follows. Firstly, the concept of odor-patch path and its estimation method are put forward. A scheme for odor source declaration is proposed, which consists of a particle-filter-based method for the source-location estimation and a statistics-based rule for the source identification. Secondly, two plume-tracking algorithms, which are upwind surge combined with modified ant colony optimization as well as Probability-fitness-function particle swarm optimization, are proposed. A three-step odor source declaration algorithm is brought forward by combining heuristic thoughts and odor mass throughput calculation. Thirdly, the least-square estimation and sub-assumption architecture based olfaction/vision fusion algorithms are proposed for localizing gas leakage sources, which could enhance the reliability of the odor source localization. All the proposed algorithms have been validated using both computer simulation and real-robot experiments.

英文关键词： Active olfaction; Turbulence dominated airflow environment; odor-patch path estimation; multiple robots; olfaction/vision fusion