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

从许多不同的角度解决了无人机轨迹计划的问题，但是当前的文献和工业公司未能为控制无人机群提供可靠的分布解决方案。为了适合可用的当前数值优化求解器，通常会近似或简化系统中有大量不确定性的复杂动力学问题。该项目的目的是构建可以以最佳分布的方式求解给定任务的动力代理，并将这些代理集成到不确定的动态环境中。这很重要，因为以集中式的方式解决大规模问题不适合固有的不稳定应用程序，在需要对控制操作的连续更新。尽管采用了现有方法，但提议的框架将考虑到多个目标：最大程度地减少能耗，最大程度地减少完成任务的时间以及最大化的可靠性。根据用户需求，可以相应地确定这些目标，而不是解决一个问题，我们将能够解决不同的相对先验炎给出的多个问题。要提供一个相关的示例问题，在该问题中，可以应用所提供的分布式数值控制算法，可以考虑到第五代网络（5G）网络中的无人机通信，可以将其置于需求和多个连接中，以满足多种需求和多个uavs uav and Uav。为了确保足够的覆盖范围，无人机需要根据用户移动重新定位。多目标优化功能非常相关，因为不同的用户可能有冲突的要求，例如，前往犯罪现场的警察团队将更加强调可靠的连接，这将使他们能够在途中收集信息，而主流用户将更感兴趣地获得较低的价格（直接链接到能源消耗和网络尺寸和网络尺寸和网络尺寸）。可以通过为自主端口操作或任何站点调查任务提供空中支持和视频监控来表示另一个潜在的用例。一般方法涉及将三种类型的动力学组合在一起，即无人机/目标动态，用户/目标移动预测和通信动态，在包括所有这些不同的管理方程式中，将其作为约束。尽管这些管理方程并不是什么新鲜事物，但尚未将它们放在相同的分布式优化问题中，并且尚未深入研究它们之间的相互作用，因为许多人都假定固定用户位置或固定的传输功率配置文件。在设计代表性模型后，下一步将是设计一个数值的算法来设计一个能够有效地在线解决问题的数值算法。我们的方法将与需要有关环境的全部知识的现有集中式算法进行比较。我们的方法可能会表现更好（在运行时），因为代理商之间的数据收集和通信很耗时。通过解决多个较低维的并行问题，我们可以将计算分开并解决无人机上嵌入式处理器上的轨迹计划问题。我们还旨在回答与系统的弹性有关的问题，例如：如果一个或多个无人机失败，将如何适应该问题，或者当无人机的数据存储/传输能力达到上限时，如何处理该问题？该项目将主要是计算的，将需要开发新的数学，而新开发的数值算法的鲁棒性保证将需要正式证实。根据可用的基础架构，可以在嵌入式平台上进行最终的物理实现