Multi Model State Estimator Design for Micro Aerial Vehicle Navigation Systems

微型飞行器导航系统多模型状态估计器设计

• 批准号: RGPIN-2017-04367
• 负责人: DeSilva, Oscar
• 金额: $ 1.68万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709983
• 关键词: Multi; Model; State; Estimator; Design

This research develops a new multi model state estimation method for navigation of Micro Aerial Vehicles (MAVs). MAVs are versatile flying platforms which are increasingly finding applications in industrial inspection, military surveillance and recreational domains. Their primary commercial applications are in the area of outdoor surveying and aerial videography. These applications almost exclusively employ GPS waypoint navigation technology and offline photogrammetry software, to autonomously generate accurate 3D maps and high resolution footage. GPS constrained inspection using MAVs is currently performed manually by radio control and often as single platform implementations. There is a growing need for developing systems and technology which can autonomously perform these routing inspection tasks while exploiting the operational efficiency of multiple MAV platforms. In this scenario an operator can specify an area of inspection of a structure, which is then surveyed autonomously by multiple MAV units while providing sensor telemetry feedback to the user. The capability relaxes the need of expert qualified pilots and waives the need of manual motion control of each platform. Multi model estimation strategy is used in applications where multiple models are needed to fully predict the behavior of a system during its operating conditions. MAV systems operate in a range of scenarios with changing sensor availability and environmental disturbances. In order to achieve accurate state estimation during these changing conditions the proposed research will develop a novel multi model estimator design for MAV navigation. The estimator will be designed while focusing on two new sources of information for the navigation system. These include the dynamic model of the platform which models the aerodynamic interactions of the device, and the relative sensing capability of MAVs which exploits the availability of multiple vehicles in a given task. The study will examine new design strategies to achieve computational efficiency, robust performance, system stability, and relative sensing methods that achieve accurate coordination between platforms. The multi model navigation system has many GPS limited industrial applications where automated surveillance and mapping are required. These include ship structure inspection, oil platform underdeck and flare inspection, and disaster surveillance. Moreover, the expected theoretical developments of the proposed research would serve a broader scientific and industrial user base, which includes underwater navigation, intelligent transport systems and flight avionics. It is expected that this research will result in a number of publications with significant impact and training of 2 PhD and 3 Master's students as part of the program.

本研究开发了一种用于微型飞行器（MAV）导航的新型多模型状态估计方法。 MAV 是多功能飞行平台，越来越多地应用于工业检查、军事监视和娱乐领域。它们的主要商业应用是户外测量和航空摄影领域。这些应用程序几乎完全采用 GPS 航路点导航技术和离线摄影测量软件，以自动生成准确的 3D 地图和高分辨率镜头。使用 MAV 的 GPS 约束检查目前通过无线电控制手动执行，并且通常作为单一平台实施。人们越来越需要开发能够自主执行这些路径检查任务的系统和技术，同时利用多个 MAV 平台的运行效率。 在这种情况下，操作员可以指定结构的检查区域，然后由多个 MAV 单元自主测量，同时向用户提供传感器遥测反馈。该功能放宽了对专业合格飞行员的需求，并免除了每个平台的手动运动控制的需要。 多模型估计策略用于需要多个模型来完全预测系统在其运行条件下的行为的应用。 MAV 系统在各种传感器可用性和环境干扰不断变化的场景中运行。为了在这些变化的条件下实现准确的状态估计，拟议的研究将为 MAV 导航开发一种新颖的多模型估计器设计。估算器的设计将重点关注导航系统的两个新信息源。其中包括对设备的空气动力学相互作用进行建模的平台动态模型，以及在给定任务中利用多个车辆的可用性的 MAV 的相对传感能力。该研究将研究新的设计策略，以实现计算效率、鲁棒性能、系统稳定性以及实现平台之间精确协调的相对传感方法。多模型导航系统有许多 GPS 限制的工业应用，其中需要自动监视和绘图。其中包括船舶结构检查、石油平台甲板下和火炬检查以及灾害监视。此外，拟议研究的预期理论发展将为更广泛的科学和工业用户群服务，其中包括水下导航、智能运输系统和飞行航空电子设备。作为该计划的一部分，预计这项研究将发表一系列具有重大影响的出版物，并培训 2 名博士生和 3 名硕士生。