Guaranteed Performance of Dynamic Behaviour of Multiple Unmanned Aerial Vehicles

多无人机动态行为的保证性能

• 批准号: EP/E048587/1
• 负责人: Antonios Tsourdos
• 金额: $ 51.43万
• 依托单位: CRANFIELD UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE048587%2F1
• 关键词: Guaranteed; Performance; Dynamic; Behaviour; Multiple

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) enhancement 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 degradation in performance. A key utility feature of a single UAV is that it is a mobile sensor platform. MUAVs offer a magnification of the sensingcapability 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 accessing guaranteed dynamic and kinematic performance of MUAVs. The dynamic behaviour of MUAVs is fundamentally different to that of a single vehicle. This pronounced difference occurs, because a group 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 achallenging 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 hierarchical character of co-operative controller of multiple UAVs; the hierarchy comprises three layers. Cooperative decision-making in Layer 1 is done by UAVs acting as a group co-operating according to common 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 controller of the corresponding UAV in Layer 3. Thus, the overall controller is obtained by co-operation of all individual controllers of the UAVs, with the cooperationdecided on Level 1, and defined by the trajectory tracking requirements in Level 2. The central 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 involve 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 scenarios. 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 integration of planning (Layer 1) andguidance (Layer 2). Imperial's RA will focus on the integration of guidance (Layer 2) and tracking control (Layer 3).

使用多个无人驾驶飞行器(MUAV)可以大大减少人力和人类在关键的安全和防御角色中面临的风险。特别是，多用途无人机可能提供：1)通过改进信息收集的延迟和准确性来扩大覆盖范围；2)提高任务成功率；3)通过增加自主性来实现新的任务和行动；4)稳健性和良性的性能下降。单架无人机的一个关键实用功能是它是一个移动传感器平台。MUAV通过创造一种机载、可重新定位的多传感器来放大传感能力。这一能力为监视和侦察任务提供了新的机会，为拟议的研究提供了实际背景。为了使潜在的好处成为现实，必须解决的一个关键问题是保证多用途无人机的动态行为性能。特别是，以可预测和可核查的方式协调多用途无人机的飞行动态，对于它们在空域的认证和对安全至关重要的任务的接受至关重要。因此，这项提案的主要重点是开发和验证一个严格的分析框架，用于推导和获取保证的MUAV的动态和运动学性能。多架无人机的动态行为与单一车辆的动态行为有根本的不同。之所以会出现这种显著的差异，是因为一组MUAV构成了一个相互关联的动力系统，这取决于单个动力学以及车辆与车辆和环境与车辆相互作用的性质。将这一动力系统表示为一个不同的实体，并对其性能进行优化是一项艰巨的研究。尽管最近取得了一些进展，但一组MUAV的实际运行还没有完全实现。拟议项目的主要目的是开发一个严格的分析框架，详细确定车辆的数量和特征、车辆之间的相互作用及其嵌入的制导和控制方案。该框架将用于预测MUAV在两个多任务任务场景中的行为，并通过模拟和地面车辆实验评估由此产生的性能。该框架用多无人机协同控制器的层次化特征来表示，该层级由三层组成。第一层中的协同决策是由无人机按照共同的使命/任务作为一个组进行协作完成的。第一层决策导致在第二层产生协作轨迹，实现对作为一个组的无人机的协调制导。协调制导产生了合作的轨迹。在第二层中产生的每个参考轨迹(制导需求)由第三层中相应无人机的单个控制器跟随。因此，总体控制器是通过无人机所有单独控制器的协作来获得的，协作在第一级确定，并由第二级的轨迹跟踪要求定义。设计的中心问题是如何设计每一层及其相互作用，以保证个体和群体的性能。本研究工作的计划将涉及两个示例场景的三层协作控制中的每一层的研究和它们的集成。两名博士生和两名博士后研究助理(RA)将为这两种情况开发新的解决方案。克兰菲尔德的博士生将开发协调制导的新算法，而帝国理工学院的博士生将专注于使用混合整数优化和参数编程方法来表达车辆的动力学。克兰菲尔德的博士后RA将专注于计划(第一层)和指导(第二层)的整合。帝国航空的RA将专注于制导(第二层)和跟踪控制(第三层)的集成。