Guaranteed Performance of Dynamic Behaviour of Multiple Unmanned Aerial Vehicles

使用多个无人驾驶飞行器（MUAV）可以显著减少人力和对关键安全和防御角色的人员风险。具体而言，MUAV潜在地提供：1）通过改进信息收集的延迟和准确性来增强大区域的覆盖; 2）增加使命成功率; 3）通过增加自主性来实现新的任务和操作; 4）性能的鲁棒性和良性降级。单个UAV的一个关键实用特征是它是一个移动的传感器平台。MUAV通过创建机载的可重新定位的多传感器来提供放大的传感能力。这种能力为监视和侦察任务提供了新的机会，为所提出的研究提供了实际的背景。为了使潜在的好处成为真实的，必须解决的一个关键问题是保证MUAV的动态性能。特别是，以可预测和可验证的方式协调MUAV的飞行动力学对于其在空域的认证和接受安全关键任务至关重要。因此，本建议书的主要重点是开发和验证一个严格的分析框架，用于推导和获得MUAV的保证动力学和运动学性能。MUAV的动力学行为构成了一个相互关联的动力学系统，这取决于个体动力学以及车辆-车辆和环境-车辆相互作用的性质。将这个动态系统表示为一个独特的实体并优化其性能是一项具有挑战性的研究。尽管最近取得了进展，一组MUAV的实际操作还没有完全实现。拟议项目的主要目标是制定一个严格的分析框架，详细确定车辆的数量和特性，车辆之间的相互作用及其嵌入式制导和控制方案。该框架将用于预测MUAV在两种多任务使命场景中的行为，并通过仿真和地面车辆实验评估由此产生的性能。该框架表示在一个层次的多无人机的合作控制器的字符，层次结构包括三层。共同执行任务。第1层决策导致在第2层中生成合作轨迹，实现无人机作为一个群体的协调制导。协调制导产生协同运行的轨迹。在第2层中生成的每个参考轨迹（制导需求）随后由UAV的各个控制器遵循，其中合作在第1层上决定，并由第2层中的轨迹跟踪需求定义。中心设计问题是如何设计各层及其相互作用，以保证个体和群体的性能，本研究工作的方案将涉及三层协同控制的每一层的研究和它们在两个示例场景中的集成。两名博士生和两名博士后研究助理（RA）将为这两个scanarios开发新的解决方案。克兰菲尔德的博士生将开发新的算法，协调指导，而帝国的博士生将专注于使用混合优化和参数编程方法来表达动态的车辆。克兰菲尔德的博士后RA将侧重于规划（第1层）和指导（第2层）的询问。帝国航空的RA将集中于制导（第2层）和跟踪控制（第3层）的集成。

The use of multiple unmanned aerial vehicles (MUAVs) can provide significant reductions in manpower and risk to humans for critical security and defence roles. In particular, MUAVs potentially offer: 1) enhancements of the coverage of large areas by improvement in latency and accuracy of information gathering; 2) increase in the mission success rate; 3) enabling new tasks and operations by increasing autonomy; 4) robustness and benign degradetion in performance. A key utility feature of a single UAV is that it is a mobile sensor platform. MUAVs offer a magnification of the sensing capability by creating an airborne, relocatable multi-sensor. This capability offers new opportunities for surveillance and reconnaissance missions which provide the practical context of the proposed research.A key issue which must be addressed in order for the potential benefits to become real is guaranteed performance of the dynamic behaviour of MUAVs. In particular, coordinating flight dynamics of MUAVs in a predictable and verifiable way is crucial for their certification in the airspace and acceptance for safety-critical missions. therefore, the main focus of this proposal is to develop and validate a rigorous, analytical framework for deriving and accesing guaranteed dynamic and kinematic performance of MUAVs. The dynamic behaviour of MUAVs constitutes an interconnected dynamical system depending on the individual dynamics and on the nature of the vehicle-vehicle and environment-vehicle interactions. The representation of this dynamical system as a distinct entity and the optimisation of its performance is a challenging study. Despite the recent advances, an actual operation of a group of MUAVs has not been fully realised yet.The main thrust of the proposed project is to develop a rigorous, analytical framework that determines in detail the number and characteristics of the vehicles, the interactions between the vehicles and their embedded guidance and control schemes. This framework will be used to predict MUAVs behaviour in two multitask mission scenarios, and assess the resulting performance through simulations and ground vehicle experiments. The framework is expressed in terms of a hierarhical character of co-operative controller of multiple UAVs; the hierarchy comprises three layers. Co-mission/tasks. Layer 1 decision-making results in generation of co-operative trajectories in Layer 2, realising coordinated guidance for the UAVs acting as a group. The coordinated guidance produces co-operating trajectories. Each reference trajectory (guidance demand) generated in Layer 2 is then followed by the individual controllers of the UAVs, with the cooperation decided on Level 1, and defined by the trajectory tracking requirements in Level 2. The centrel design issue is how to design each layer and their interaction, so that the individual and group performance can be guaranteed.The programme of this research work will ivolve both the study of each of three layers of cooperative control and the integration of them for two example scenarios. Two PhD students and two post doctoral research associates (RA) will develop new solutions for both scanarios. Cranfield's PhD student will develop new algorithms for coordinated guidance, while Imperial's PhD student will focus on the use of Mixed-Integer Optimization and Parametric Programming methods to express dynamics of the vehicles. Cranfield's post-doc RA will focus on the interogation of planning (Layer 1) and guidance (Layer 2). imperial's RA will focus on the integration of guidance (Layer 2) and tracking control (Layer 3).