融合机器人视/嗅觉信息的气体泄漏源定位
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摘要
科学研究表明,很多生物利用气味或/和视觉等信息完成觅食、求偶、御敌和交流等活动。受这些生物行为的启发,自20世纪90年代开始,一些学者开始利用移动机器人结合气体、视觉等传感器进行气体泄漏源定位研究。移动机器人气体泄漏源定位的研究成果有望在不远的将来被用于有毒/有害气体泄漏检测、火源探测、灾后倒塌建筑物搜救和反恐排爆等领域。
本文针对机器人融合视/嗅觉信息搜寻气体泄漏源问题,重点开展了如下的研究工作:
一、针对现有机器人泄漏源定位过程中视觉信息处理存在的不足,本文提出了一种新的任务驱动视觉注意机制计算模型(即TDVAM计算模型)用于视觉信息的快速处理。通过学习的方法确定凸显目标物的重要特征和最佳尺度,将有限的计算和存储资源优先分配给场景中少数的显著区域,快速地凸显特定的搜寻目标,满足机器人视觉信息处理的实时性需求。针对待搜寻目标的精确识别问题,提出了形状分析与TDVAM计算模型相结合的目标识别方法,通过提取候选显著区域的多个形状特征(如面积、周长、紧密度等)判断该区域是否是疑似泄漏源区域,并与传统的模板匹配方法进行了比较,验证了本文所提方法的可靠性。
二、针对风速/风向比较稳定的流场环境中气体泄漏源定位问题,提出了基于最小二乘估计的机器人融合视/嗅觉信息的泄漏源定位方法。此类环境中气体浓度近似高斯分布。首先通过视觉确定出场景中存在的可疑区域,然后针对每一个可疑区域采用湍流扩散模型计算出机器人在采样点处的理论浓度值,最后采用最小二乘方法对机器人获取的实际浓度值和理论浓度值进行最小偏差估算,快速确认泄漏源的位置。真实机器人实验验证了本文所提方法的有效性。
三、针对风速/风向变化比较大的环境中气体泄漏源定位问题,提出了基于包容体系结构的机器人融合视/嗅觉信息泄漏源定位方法。在此类环境中由于受湍流的影响气体浓度的分布很难用一个精确的数学模型来描述。本文针对机器人在定位过程中获取传感器信息的不同建立具有不同优先级的行为策略,高优先级的行为能够对低一级的行为进行包含和抑制,使得机器人能够实时地产生一个优化策略,面对动态、复杂、非结构化环境做出快速反应并完成任务。真实机器人实验验证了本文所提方法的可靠性与鲁棒性。
Research results show that many animals use olfaction or/and vision cues to search for food, find same species, evade predators, exchange information and so on. Inspired by those biological activities, in the early 1990s researchers started to build mobile robots with onboard gas sensors or/and visual sensor to accomplish the gas-source-localization task. It is expected that such research results 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 attacks.
This dissertation focuses on the mobile robot based gas source localization by fusing vision and olfaction information. The achievements can be concluded as follows.
Firstly, in view of the drawbacks of the existing visual information processing methods for the robot based gas source localization, a novel top-down visual attention mechanism (TDVAM) computation model is proposed. The important features and the optimal scales of the salient objects are determined via learning. Since the limited computing and memory resources of the microprocessor are mainly used for processing the salient objects, it can meet the real-time requirement of the mobile robot. Meanwhile, shape analysis method is combined with the TDVAM computation model in order to recognize the object more accurately. Several shape features, including area, perimeter and compactness, are extracted to identify whether the candidate salient regions are the plausible areas or not. The shape analysis method is also compared with the template matching method. Experimental results validate the accuracy and reliability of the proposed method.
Secondly, for the gas source localization in relatively stable airflow environments in which both the wind speed and direction have no large-scale fluctuation, a novel vision/olfaction fusion method based on least square estimation is put forward. In such environments, the gas concentration approximates to Gaussian distribution. The plausible areas are determined using vision information, and the theoretical gas concentration of all the sampled locations in every plausible area can be calculated using the turbulent–diffusion model. The minimum deviation between the theoretical gas concentration and the real one is estimated using least square method to declare which plausible area is the real source. Real-robot experimental results demonstrate the efficiency of the proposed method.
Thirdly, the subsumption architecture based vision/olfaction fusion method is presented to accomplish the gas source localization task in the airflow environments where both the wind speed and direction have relatively large-scale fluctuation. In such environments, it is difficult to describe the distribution of the gas concentration using a mathematic model due to the influence of turbulence. In order to make full use of the multi-sensor information, the different behavior strategies with different priorities are set up. The behavior with higher priority can subsume or inhibit the behavior with lower priority, which makes the robot generate an optimization strategy to deal with the dynamic, complex and unstructured environments. With the subsumption architecture and the gas source localization task could be accomplished efficiently. The reliability and robustness of the proposed method are validated with the real robot experiments.
本文针对机器人融合视/嗅觉信息搜寻气体泄漏源问题,重点开展了如下的研究工作:
一、针对现有机器人泄漏源定位过程中视觉信息处理存在的不足,本文提出了一种新的任务驱动视觉注意机制计算模型(即TDVAM计算模型)用于视觉信息的快速处理。通过学习的方法确定凸显目标物的重要特征和最佳尺度,将有限的计算和存储资源优先分配给场景中少数的显著区域,快速地凸显特定的搜寻目标,满足机器人视觉信息处理的实时性需求。针对待搜寻目标的精确识别问题,提出了形状分析与TDVAM计算模型相结合的目标识别方法,通过提取候选显著区域的多个形状特征(如面积、周长、紧密度等)判断该区域是否是疑似泄漏源区域,并与传统的模板匹配方法进行了比较,验证了本文所提方法的可靠性。
二、针对风速/风向比较稳定的流场环境中气体泄漏源定位问题,提出了基于最小二乘估计的机器人融合视/嗅觉信息的泄漏源定位方法。此类环境中气体浓度近似高斯分布。首先通过视觉确定出场景中存在的可疑区域,然后针对每一个可疑区域采用湍流扩散模型计算出机器人在采样点处的理论浓度值,最后采用最小二乘方法对机器人获取的实际浓度值和理论浓度值进行最小偏差估算,快速确认泄漏源的位置。真实机器人实验验证了本文所提方法的有效性。
三、针对风速/风向变化比较大的环境中气体泄漏源定位问题,提出了基于包容体系结构的机器人融合视/嗅觉信息泄漏源定位方法。在此类环境中由于受湍流的影响气体浓度的分布很难用一个精确的数学模型来描述。本文针对机器人在定位过程中获取传感器信息的不同建立具有不同优先级的行为策略,高优先级的行为能够对低一级的行为进行包含和抑制,使得机器人能够实时地产生一个优化策略,面对动态、复杂、非结构化环境做出快速反应并完成任务。真实机器人实验验证了本文所提方法的可靠性与鲁棒性。
Research results show that many animals use olfaction or/and vision cues to search for food, find same species, evade predators, exchange information and so on. Inspired by those biological activities, in the early 1990s researchers started to build mobile robots with onboard gas sensors or/and visual sensor to accomplish the gas-source-localization task. It is expected that such research results 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 attacks.
This dissertation focuses on the mobile robot based gas source localization by fusing vision and olfaction information. The achievements can be concluded as follows.
Firstly, in view of the drawbacks of the existing visual information processing methods for the robot based gas source localization, a novel top-down visual attention mechanism (TDVAM) computation model is proposed. The important features and the optimal scales of the salient objects are determined via learning. Since the limited computing and memory resources of the microprocessor are mainly used for processing the salient objects, it can meet the real-time requirement of the mobile robot. Meanwhile, shape analysis method is combined with the TDVAM computation model in order to recognize the object more accurately. Several shape features, including area, perimeter and compactness, are extracted to identify whether the candidate salient regions are the plausible areas or not. The shape analysis method is also compared with the template matching method. Experimental results validate the accuracy and reliability of the proposed method.
Secondly, for the gas source localization in relatively stable airflow environments in which both the wind speed and direction have no large-scale fluctuation, a novel vision/olfaction fusion method based on least square estimation is put forward. In such environments, the gas concentration approximates to Gaussian distribution. The plausible areas are determined using vision information, and the theoretical gas concentration of all the sampled locations in every plausible area can be calculated using the turbulent–diffusion model. The minimum deviation between the theoretical gas concentration and the real one is estimated using least square method to declare which plausible area is the real source. Real-robot experimental results demonstrate the efficiency of the proposed method.
Thirdly, the subsumption architecture based vision/olfaction fusion method is presented to accomplish the gas source localization task in the airflow environments where both the wind speed and direction have relatively large-scale fluctuation. In such environments, it is difficult to describe the distribution of the gas concentration using a mathematic model due to the influence of turbulence. In order to make full use of the multi-sensor information, the different behavior strategies with different priorities are set up. The behavior with higher priority can subsume or inhibit the behavior with lower priority, which makes the robot generate an optimization strategy to deal with the dynamic, complex and unstructured environments. With the subsumption architecture and the gas source localization task could be accomplished efficiently. The reliability and robustness of the proposed method are validated with the real robot experiments.
引文
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