Mobile Robotics & Olfaction Lab, AASS Research Center, School of Science and Technology, Örebro University, 702 81 Örebro, Sweden.
Sensors (Basel). 2019 Feb 7;19(3):685. doi: 10.3390/s19030685.
Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.
应急人员，如消防员、炸弹技术人员和城市搜索和救援专家，在应对自然灾害和人为灾害时，可能会接触到各种极端危险。在许多情况下，一个危险因素是存在有害空气化学物质。机器人技术和传感器技术的最新快速发展使应急人员能够在相对安全的距离内应对这些危险。具有气体感应能力的移动机器人可以传达有用的信息，例如紧急区域中不同化学物质的可能源位置。然而，由于环境的复杂性和快速部署和分析的需要，实验室常用的气体采样程序并不适用。此外，基于监督学习的常规气体识别方法无法处理当前化学物质的数量和身份未知的情况。出于应急响应的目的，应在实时将机器人探索过程中从气体检测事件中得出的所有信息传递。为了解决这些挑战，我们开发了一种使用电子鼻的在线气体感应系统。我们的系统可以在机器人探索给定环境时自动执行无监督学习并更新判别模型。在线气体判别结果进一步与几何信息集成，以得出多化合物气体空间分布图。所提出的系统部署在为支持消防队而构建的可在恶劣环境中运行的机器人上，并在室内开放环境中对多种化学物质进行判别和映射的几个不同真实实验中进行了验证。我们的结果表明，所提出的系统以在线、无监督和计算高效的方式实现了气体判别中的高精度。随后创建的气体分布地图准确地指示了环境中不同化学物质的存在，这对应急响应具有实际意义。
