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

人们从许多不同的角度来解决无人机轨迹规划问题，但是当前的文献和工业公司未能提供可靠的分布式解决方案来控制无人机群。系统中具有大量不确定性的复杂动态问题通常会被近似或简化，以适应当前可用的数值优化求解器。该项目的目的是构建动态代理，能够以最佳分布方式解决给定任务，并将这些代理集成到不确定的动态环境中。这是相关的，因为以集中方式解决大规模问题并不适合需要持续更新控制操作的本质上不稳定的应用。尽管存在现有方法，拟议的框架仍将考虑多个目标：最大限度地减少能源消耗、最大限度地减少完成任务的时间以及最大限度地提高可靠性。根据用户需求，可以对这些目标进行相应的优先级排序，我们将能够解决由不同相对优先级给出的多个问题，而不是解决单个问题。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，需要使用多个无人机来增强连接性。为了确保足够的覆盖范围，无人机需要根据用户的移动重新定位。多目标优化功能非常重要，因为不同的用户可能会有相互冲突的需求，例如，前往犯罪现场的警察团队会更注重可靠的连接，以便他们能够在途中收集信息，而主流用户会更注重可靠的连接，从而使他们能够在途中收集信息。对获得更低的价格更感兴趣（这与能源消耗和网络规模直接相关）。另一个潜在的用例可以通过为自主港口运营或任何现场检查任务提供空中支持和视频监控来代表。一般方法包括将三种类型的动态结合在一起，即无人机动态、用户/目标运动预测和通信动态。包含所有这些不同的控制方程作为约束的模拟环境。虽然这些控制方程并不新鲜，但它们尚未放在同一分布式优化问题中，并且它们之间的相互作用尚未得到深入研究，因为许多人假设固定的用户位置或固定的传输功率分布。设计了代表性模型后，下一步将是设计一种能够实时有效地在线解决问题的数值算法。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。我们的方法可能会执行得更好（就运行时间而言），因为代理之间的数据收集和通信非常耗时。通过解决多个低维并行问题，我们可以拆分计算并解决无人机机载嵌入式处理器上的轨迹规划问题。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，其余无人机应如何适应这种情况，或者当无人机的数据存储/传输能力下降时应如何处理情况达到上限了吗？该项目将主要是计算性的，需要开发新的数学，其中新开发的数值算法的鲁棒性保证需要得到正式证明。该项目的输出将通过实际用例的数值模拟来表示，以证明我们的方法的有效性和适用性。最终可以在嵌入式平台上进行物理实现，具体取决于可用的基础设施