Distributed numerical optimal control of unmanned aerial vehicle (UAV) networks

无人机网络的分布式数值优化控制

• 批准号: 2466865
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2466865
• 关键词: Distributed; numerical; optimal; control; unmanned

The problem of UAV trajectory planning has been approached from many different perspectives, however current literature and industrial companies fail to provide a reliable distributed solution for controlling UAV swarms. Complex dynamical problems with a significant amount of uncertainty in the system are often approximated or simplified in order to fit the current numerical optimization solvers available. The aim of this project is to construct dynamical agents that can solve given tasks in an optimally distributed manner and integrate these agents into an uncertain, dynamic environment. This is relevant because solving a large-scale problem in a centralized way is not suited for inherently unstable applications where a continuous update of the control action is needed. Despite existing approaches, the proposed framework will have multiple objectives in mind: minimise energy consumption, minimise time to complete the mission as well as maximise reliability. Based on user needs, these objectives can be prioritised accordingly and, instead of solving a single problem, we would be able to solve multiple problems given by different relative prioritisations.To give a relevant example problem where the presented distributed numerical control algorithms can be applied, consider UAV communications in fifth generation(5G) networks.Stationary nodes may not be able to meet the demand and multiple UAVs will need to be used to enhance the connectivity. In order to ensure sufficient coverage, UAVs need to reposition themselves based on user movement. The multi-objective optimization feature is extremely relevant since different users may have conflicting requirements, for example a police team travelling to a crime scene will put more emphasis on reliable connection that will enable them to gather information on the way, while a mainstream user will be more interested in getting lower price (which is directly linked to energy consumption and network size). Another potential use case can be represented by providing aerial support and video monitoring for autonomous port operations or any site-inspection task.The general methodology involves putting together three types of dynamics, namely UAV dynamics, user/target movement prediction and communication dynamics, in a simulation environment that includes all these different governing equations as constraints. While these governing equations are not new, they have not yet been put together in the same distributed optimization problem and the interaction between them has not been studied in-depth, since many people assume either fixed user positions, or fixed transmission power profiles.After designing a representative model, the next step would be to design a numerical algorithm that is able to efficiently solve the problem online in real-time. Our method will be compared against existing centralized algorithms that require full knowledge about the environment. Our method is likely to perform better (in terms of runtime), since data gathering and communications between agents is time consuming. By solving multiple lower-dimensional parallel problems, we can split the computation and solve the trajectory planning problem on the UAVs' on-board embedded processors. We also aim to answer questions related to the system's resilience, such as: what happens if one or more UAVs fail, how should the remaining ones adapt to this, or how should one deal with situations when the data storage/transmission capacity of a drone hits the upper limit? The project will mainly be computational, with novel mathematics to be developed where the robustness guarantees of the newly developed numerical algorithm will need to be formally proven.The output of the project will be represented by numerical simulations of practical use cases in order to prove the effectiveness and applicability of our approach. Eventual physical implementation on embedded platforms is possible, depending on the infrastructure available

无人机的轨迹规划问题已经从许多不同的角度进行了探讨，然而目前的文献和工业公司未能提供一个可靠的分布式解决方案来控制无人机群。对于系统中具有大量不确定性的复杂动力学问题，通常采用近似或简化的方法来拟合现有的数值优化解。这个项目的目的是构建能够以最优分布的方式解决给定任务的动态代理，并将这些代理集成到一个不确定的动态环境中。这是相关的，因为以集中的方式解决大规模问题不适合本质上不稳定的应用程序，因为这些应用程序需要持续更新控制操作。尽管现有的方法，拟议的框架将有多个目标：最小化能源消耗，最小化完成任务的时间，以及最大化可靠性。根据用户的需求，这些目标可以相应地进行优先级排序，而不是解决单个问题，我们将能够解决由不同的相对优先级给出的多个问题。为了给出一个可以应用所提出的分布式数控算法的相关示例问题，考虑第五代（5G）网络中的无人机通信。固定节点可能无法满足需求，需要使用多架无人机来增强连接。为了保证足够的覆盖范围，无人机需要根据用户的移动来重新定位自己。多目标优化功能是非常相关的，因为不同的用户可能有相互冲突的需求，例如，一个警察团队前往犯罪现场将更强调可靠的连接，使他们能够在途中收集信息，而一个主流用户将更感兴趣的是获得更低的价格（这与能源消耗和网络规模直接相关）。另一个潜在的用例是为自主港口作业或任何现场检查任务提供空中支持和视频监控。一般的方法包括将三种类型的动力学放在一起，即无人机动力学，用户/目标运动预测和通信动力学，在包括所有这些不同的控制方程作为约束的仿真环境中。虽然这些控制方程并不新鲜，但由于许多人要么假设固定的用户位置，要么假设固定的传输功率分布，因此还没有将它们放在同一个分布式优化问题中，也没有对它们之间的相互作用进行深入研究。在设计了一个有代表性的模型之后，下一步就是设计一个能够在线实时有效地求解问题的数值算法。我们的方法将与需要充分了解环境的现有集中式算法进行比较。我们的方法可能执行得更好（就运行时而言），因为数据收集和代理之间的通信非常耗时。通过求解多个低维并行问题，可以将计算拆分，在无人机机载嵌入式处理器上求解轨迹规划问题。我们还旨在回答与系统弹性相关的问题，例如：如果一架或多架无人机发生故障会发生什么，其余的无人机应该如何适应这种情况，或者当无人机的数据存储/传输容量达到上限时应该如何处理？该项目将主要是计算性的，需要开发新的数学，其中需要正式证明新开发的数值算法的鲁棒性保证。项目的输出将通过实际用例的数值模拟来表示，以证明我们方法的有效性和适用性。最终在嵌入式平台上的物理实现是可能的，这取决于可用的基础设施