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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679346
• 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.

这项研究开发了一种新的多模型状态估计方法，用于导航微型航空车（MAV）。 MAV是多功能的飞行平台，越来越多地在工业检查，军事监视和休闲域中找到应用。它们的主要商业应用是在室外测量和航空摄影领域。这些应用程序几乎完全采用GPS Waypoint导航技术和离线摄影测量软件，以自主生成准确的3D地图和高分辨率录像。当前，使用MAV进行了GPS限制的检查，目前是通过无线电控制手动执行的，通常是单个平台实现。开发系统和技术的需求越来越大，可以自主执行这些路由检查任务，同时利用多个MAV平台的运行效率。 在这种情况下，操作员可以指定检查结构的区域，然后由多个MAV单元自主对其进行调查，同时向用户提供传感器遥测反馈。能力放松了合格的飞行员的需求，并放弃了每个平台手动运动控制的需求。 ***多模型估计策略用于需要多个模型以完全预测系统在其操作条件下的行为的应用。 MAV系统在一系列方案中运行，并随着传感器的可用性和环境干扰而变化。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导航开发一种新型的多模型估计器设计。估计器将在关注导航系统的两个新信息来源时进行设计。其中包括模拟设备空气动力相互作用的平台的动态模型，以及MAV的相对感应能力，从而利用了给定任务中多个车辆的可用性。该研究将研究新的设计策略，以实现计算效率，稳健性能，系统稳定性以及相对感应方法，以实现平台之间的准确协调。多模型导航系统具有许多GPS有限的工业应用，需要自动监视和映射。其中包括船舶结构检查，石油平台底漆和耀斑检查以及灾难监视。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提供服务，其中包括水下导航，智能运输系统和飞行航空电子学。预计这项研究将导致许多出版物，并作为该计划的一部分对2位博士学位和3位硕士学生进行了重大影响和培训。