控制器-应答机器人的新型气味感应智能和监视能力

Serena Gandhi, Ajith Abraham
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引用次数: 0

摘要

随着全球旅行的增加,人们越来越需要加强机场的安全措施。尽管机场安检人员尽了最大努力,但在过去一年中,仍有数百公斤非法毒品和数千种农业入侵物种流入我国,对公共安全和环境构成了严重威胁。此外,人为威胁也对民航构成了重大风险,这就更加需要先进的安全技术。为了应对这些挑战,我们开发了 NOSI(新型气味传感智能系统)和 ROSI(侦察行动安全智能系统)这两个由半自动控制应答机器人组成的智能监控系统,作为对机场安全和 K-9(警犬)操作人员工作的补充。NOSI 配备了用于气味检测的多通道气体传感器,能够在行李处理过程中识别非法毒品和入侵物种,而 ROSI 则配备了计算机视觉系统,能够识别政府数据库中的涉案人员。这些相互配合的机器人还能为旅客提供与行程相关的重要信息,并能触发紧急警报。这些机器人在一个定制设计的试验台中进行测试,该试验台复制了机场幕后行李处理和前台客户服务操作,从而模拟了类似机场的真实环境。根据设计标准,NOSI 和 ROSI 的成功率分别为 73.4% 和 69.8%。在机器人稳定性、传感器精度和功能扩展方面的改进已记录在案,有待进一步开发。总之,NOSI 和 ROSI 框架可以提高机场基础设施监控的效率和准确性,并补充人类和 K9 操作员的能力。总之,这种方法有可能彻底改变各种基础设施的运行状况,代表着人类与机器人协作的未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel odor sensing intelligence and surveillance capabilities in controller-responder robots
The rise in global travel has led to an increased need for heightened security measures at airports. Despite the best efforts of airport security officers, in the past year, hundreds of kilograms of illegal drugs and thousands of agricultural invasive species have found their way into the country, posing a severe threat to public safety and the environment. Moreover, human threats pose a significant risk to civil aviation, reinforcing the need for advanced security technology. In response to these challenges, NOSI (Novel Odor Sensing Intelligence) and ROSI (Reconnaissance Operations Security Intelligence), intelligence surveillance systems consisting of semi-autonomous controller-responder robots, were developed as a proof of concept to supplement the efforts of security and K-9 (police dogs) operators at airports. NOSI is equipped with multi-channel gas sensors for odor detection, enabling it to identify illegal drugs and invasive species in the baggage handling process, while ROSI is equipped with computer vision to identify individuals already in the government’s database of persons of interest. These coordinated robots also provide travelers with important information pertaining to their journey and allow them to trigger emergency alerts. The robots were tested in a custom-designed test bed that replicated both the behind-the-scenes baggage handling and front-office customer service operations of an airport, thus simulating a realistic airport-like setting. Based on design criteria, NOSI and ROSI demonstrated success rates of 73.4 percent and 69.8 percent, respectively. Improvements in areas of robot stability, sensor accuracy, and feature expansion were documented for further development. In conclusion, the NOSI and ROSI framework can enhance the efficiency and accuracy of airport infrastructure monitoring and supplement the capabilities of human and K9 operators. Overall, this approach can potentially revolutionize operations in various infrastructures and represents the future of human-robot collaboration.
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