Machine Learning Driven Search and Rescue.

不幸的是，搜救弱势失踪人员是警察和其他紧急服务部门的一项共同任务。像搜索和救援中心（CSR） b[1]这样的组织对失踪人员（幼儿，老年痴呆症患者等）的典型行为模式进行研究，并为与SAR相关的所有领域的警察部队提供专业培训。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第一批利用这项技术的紧急服务人员之一，他们在商用四旋翼飞机上使用红外和可见光摄像头，作为目前单人驾驶直升机的辅助。目前的计划是使用单个无人机平台搜索一个预先定义的区域，探测可能的目标并指挥搜索队，尽管未来也考虑使用蜂群车辆。在一些概念验证研究中，采用遗传算法和粒子群优化等数值优化方法来确定最优路径。与经典的平行路径（割草机模式）相比，这些路径显示出更快的搜索时间，甚至与训练有素的苏格兰警察空中支援部队操作员相比。该研究项目旨在通过使用强人工智能和机器学习，扩展和自动化已经获得的非常有希望的结果，即人工创建概率图。特别是，我们的研究假设是，模糊逻辑和深度学习的结合可以用来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能力结合起来。使用人工智能/机器学习的主要目标是创建自动概率图创建的新算法，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。

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.