Machine Learning Driven Search and Rescue.

机器学习驱动的搜索和救援。

• 批准号: 2605677
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2605677
• 关键词: Machine; Learning; Driven; Search; Rescue.

Search and Rescue (SAR) of vulnerable missing persons is unfortunately a common task for the Police and otheremergency services. Organisations like the Centre for Search and Rescue (CSR)[1] carry out research into thetypical behaviour patterns for classes of missing person (young child, elderly person with dementia etc.) andprovide specialist training to Police forces in all areas related to SAR. With the rapid development and expansion of the drone sector over the last decade, Unmanned Aerial Vehicles (UAVs) have become cheaper and more accessible than ever before. Officers from the Air Support Unit of Police Scotland are one of the first emergency services in the UK to take advantage of this technology by using infrared and visible light cameras on commercially available quadrotors as an aid to the current single manned helicopter. Current plans are to use single UAV platforms to search a pre-defined area, to detect possible targets and direct the search team, although swarms of vehicles are also being considered for future use. During some proof-of-concept research, numerical optimisation methods such as genetic algorithms and particle swarm optimisation were used to determine optimal paths. These paths were shown to yield faster search times when compared to the classic parallel swaths (lawnmower pattern) and even compared to trained Police Scotland Air Support Unit operators. This research project intends to extend and automate the extremely promising results already obtained using manual creation of probability maps through the use of strong AI and machine learning. In particular, our research hypothesis is that a combination of fuzzy logic and deep learning could be used to combine the psychological descriptors of missing person classes (which are natural-language based) with the image classification capabilities of deep learning neural networks. The primary objective of using AI/machine learning would be to create new algorithms for automatic probability map creation, but we will also explore the benefits of using these techniques to replace, augment or reduce computation time of the path optimisation processes. The effectiveness of single and multiple UAVs will be explored, as will the constraints imposed by holonomic and non-holonomic platforms.

不幸的是，对弱势失踪人员的搜索和救援是警察和其他紧急服务部门的共同任务。搜索和救援中心（CSR）[1]等组织对各类失踪人员（幼儿，老年痴呆症患者等）的典型行为模式进行了研究。并为警队提供与搜救有关的专业训练。随着无人机行业在过去十年的快速发展和扩张，无人机（UAV）变得比以往任何时候都更便宜，更容易获得。苏格兰警察空中支援部队的官员是英国首批利用这项技术的紧急服务机构之一，他们在商用四旋翼上使用红外和可见光摄像机，作为目前单人直升机的辅助设备。目前的计划是使用单个无人机平台搜索预定义的区域，以发现可能的目标并指导搜索团队，尽管未来也考虑使用成群的车辆。在一些概念验证研究中，使用遗传算法和粒子群优化等数值优化方法来确定最佳路径。与经典的平行条带（割草机模式）相比，甚至与训练有素的苏格兰警察空中支援部队操作员相比，这些路径显示出更快的搜索时间。该研究项目旨在通过使用强大的人工智能和机器学习来扩展和自动化已经使用手动创建概率图获得的非常有前途的结果。特别是，我们的研究假设是，模糊逻辑和深度学习的组合可以用于将失踪人员类别的心理描述符（基于自然语言）与深度学习神经网络的图像分类功能相结合。使用人工智能/机器学习的主要目标是为自动概率图创建创建新的算法，但我们也将探索使用这些技术来取代，增加或减少路径优化过程的计算时间的好处。将探讨单个和多个无人机的有效性，以及完整和非完整平台所施加的约束。