Automated UAV Mission Optimisation Using an On-Board Digital Twin for Long Range Wildlife Conservation Tasks

布里斯托尔大学飞行实验室将把重点扩展到远程无人机行动，以保护动物，这是从2023年1月开始的WildDrone Marie Curie Network赠款的一部分。这结合了超视距(BVLOS)操作、飞机控制和飞机设计方面的专业知识，以完成极其苛刻的任务。与此同时，飞行教练项目正在致力于利用先进的飞行数据后处理能力自动分析精确的特技飞行。这项工作将这两个项目结合在一起，基于对飞行数据的连续、自动分析来优化固定翼无人驾驶飞行器(UAV)任务。在开发高效无人机以及预测其性能的工具方面存在着重大工作。与当地风速相比，这些无人机在相对较低的飞行速度下运行，这意味着它们的潜在任务性能与所选的运行参数密切相关。尽管如此，无人机的作战决策往往只有限地考虑性能模型和当地环境。这项研究旨在缩小这一差距，使性能模型估计和风速估计自动化，以便实时提供任务参数和飞行路线优化。作为Flight Coach项目的一部分开发的流程包括使用时间比对算法收集和标记飞行数据。这一过程对于飞行状态识别(FRR)来说是非常宝贵的。用于FRR的方法有很多种，其中之一是用标记的训练数据训练神经网络，以基于状态观测来预测状态。其他研究表明，使用低成本飞行数据记录器的FRR是估计飞机部件载荷的有效方法，甚至可以比直接在被分析部件上安装应变传感器更可取。基于这一过程开发的训练有素的FRR代理将被部署在飞机上，以自动识别稳定状态和操纵飞行状态。实时参数辨识和模型估计是目前研究较多的领域，各种技术已经存在。这些方法将被审查、选择并部署在具有代表性的试验台上，以期将流程和系统集成标准化，作为ArduPilot开放源码自动驾驶系统的补充。部署在机载同伴计算机上的这一过程的潜在效用超出了上述任务和性能优化的范围，特别是通过更早地检测故障来增强无人机操作的安全性。为了产生可靠的任务优化，必须对预计空域的环境条件进行估计。通过利用不断更新的飞机参数模型和IMU测量，可以实现对无人机的本地、机载风估计。还将开发将低层测量外推到更大高度范围的方法。这项工作在这一领域的新贡献是实施并结合这两项任务，以提供随着任务进展而改善的预测风场。该项目的目的是开发基于飞行数据观测的自动化方法来优化固定翼无人机的任务性能。提出了以下目标：-使用在稳态飞行和快速机动期间对自动标记的飞行数据进行训练的神经网络来识别关键的飞行状态。-定义并自动化实时参数识别(RTPID)过程，与实时风场估计相结合，以提供持续更新的机载数字孪生兄弟。-将数字孪生和环境估计应用于实时任务参数优化，以最大限度地减少远程测量任务中的能源消耗。-扩大范围，以考虑特定的个体动物识别任务，自动生成路径和航向……

The University of Bristol Flight Lab is extending its focus to long range UAV operations for animal conservation as part of the WildDrone Marie Curie Network grant starting in January 2023. This brings together expertise in beyond visual line of sight (BVLOS) operations, aircraft control and aircraft design to achieve extremely demanding missions. In parallel, the Flight Coach Project is working to automate analysis of precision aerobatic flights with advanced flight data post-processing capabilities. This work brings the two projects together to optimise fixed wing Uninhabited Aerial Vehicle (UAV) missions based on continuous, automated analysis of flight data. Significant work exists in the development of highly efficient UAVs as well as in tools to predict their performance. These UAVs operate at relatively low flight speeds in relation to the local wind speeds, meaning that their potential mission performance is strongly related to the chosen operating parameters. Despite this, operational decisions for UAVs often take only limited consideration of the performance model and the local environment. This research aims to close this gap, automating the performance model estimation and wind estimation to feed into real time mission parameter and flight path optimisation. Part of the process developed as part of the Flight Coach project includes collecting and labelling flight data using a temporal alignment algorithm. This process is invaluable for Flight Regime Recognition (FRR). A variety of methods are used for FRR, one of which is to train a neural network with labelled training data to predict the regime based on state observations. Other research shows that FRR using low cost flight data recorders is an effective method of estimating loads on aircraft components, and can even be preferable to directly mounting strain sensors on the component being analysed. The trained FRR agents developed based on this process will be deployed on-board to automatically recognise steady state and manoeuvring flight regimes. Real Time Parameter Identification (RTPID) and model estimation are well researched areas and various techniques already exist. These methods will be reviewed, selected and deployed on a representative test bed, with a view to standardising the processes and systems integration as an add on to the Ardupilot open source autopilot system. The potential utility of this process deployed on an onboard companion computer extends beyond the mission and performance optimisations described above, especially to enhancing safety of UAV operations by detecting failures earlier. To produce reliable mission optimisations, estimates of the environmental conditions in the projected airspace must be produced. Local, on-board wind estimation can be achieved for a UAV by utilising the continuously updated aircraft parameter model and IMU measurements. Methods of extrapolating low level measurements to larger height ranges will also be developed. The novel contribution of this work in this area is to implement and couple these two tasks to provide an improving projected wind field as the mission progresses. The aim of this project is to develop automated methods to optimise fixed wing UAV mission performance based on flight data observations. The following objectives are proposed:-Identify key flight regimes using neural networks trained on automatically labelled flight data during steady state flight and rapid manoeuvres.-Define and automate a real time parameter identification (RTPID) process, coupled with live wind field estimation to provide a continuously updated on-board digital twin.-Apply the digital twin and environment estimate to real time mission parameter optimisation to minimise energy usage in long range surveying missions.-Extend the scope to consider specific individual animal identification tasks, to automate path generation and course...