Multi-Sensory UAV Navigation Research Testbed

多感官无人机导航研究试验台

• 批准号: RTI-2021-00248
• 负责人: DeSilva, Oscar
• 金额: $ 5.39万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 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=717811
• 关键词: Multi; Sensory; UAV; Navigation; Research

This proposal seeks funding to acquire infrastructure to support multi-model robotic navigation system design research at the Intelligent Systems Lab of Memorial University of Newfoundland. Unmanned Aerial Vehicles (UAVs) are servicing several industrial, survey, and research sectors in both remotely operated and autonomous settings. Currently, these applications employ GPS waypoint navigation control to service inspection tasks required by clients. However, for many industrial applications, precise autonomous operation under intermittent and limited GPS conditions is desired in varying environmental conditions, e.g., structural inspection tasks at sites with areas of patchy GPS reception, offshore structures inspection, dry dock inspection of ships, operational scale drone delivery, and environmental surveillance to support harsh environment navigation. These applications demand a multi-sensory and multi-model approach with robust redundancy measures to achieve safety-critical autonomy of the platforms. The research program has both fundamental knowledge development and operational scale validation where an archive of system and sensor models is used in a consistent way to reliably observe the navigation states of robots, i.e., aerial robots transitioning through GPS limited areas, through pre-mapped regions, and areas with changing environmental conditions such as wind gust, magnetic disturbances, and degraded visibility. The research methodology examines probabilistic estimator design that employs a bank of models at its disposal that include nominal Inertial Navigation System (INS) mechanization equations, dynamic constraints of the platform, learned un-modeled dynamics, and models with varying stochastic noise parameters. Furthermore, the existence of redundant sensor sources ranging from visual, inertial, Ultra-Wide Band, LiDAR, and GPS are investigated to achieve robust navigational system performance for extending platforms' operational envelope. This NSERC RTI proposal requests equipment support for a “Multi-sensory UAV navigation research testbed” which comprises of a hexacopter drone equipped with a custom visual-inertial-Lidar navigation sensor package and a RTK GPS system. The proposed equipment is necessary to complete field experimental validation of multi-model robotic navigation research findings using actual sensor data feedback during operation. The equipment will provide HQP hands-on training on operational scale validation of UAV navigation technology using state of the art software and hardware tools. These capabilities are highly transferable to technology and research sectors across Canada and significantly enhance the industry collaborative research partnerships of MUN ISL.

该提案寻求资金购买基础设施，以支持纽芬兰纪念大学智能系统实验室的多模型机器人导航系统设计研究。无人机 (UAV) 在远程操作和自主环境中为多个工业、调查和研究部门提供服务。目前，这些应用程序采用 GPS 航路点导航控制来服务客户所需的检查任务。然而，对于许多工业应用来说，在不同的环境条件下，需要在间歇性和有限的 GPS 条件下进行精确的自主操作，例如，在 GPS 接收不完整的区域执行结构检查任务、海上结构检查、船舶干船坞检查、操作规模的无人机交付以及支持恶劣环境导航的环境监视。这些应用需要采用多传感器和多模型方法以及强大的冗余措施，以实现平台的安全关键自主性。 该研究项目既有基础知识开发，也有业务规模验证，其中以一致的方式使用系统和传感器模型的档案来可靠地观察机器人的导航状态，即空中机器人穿过GPS有限区域、预先绘制的区域以及环境条件不断变化的区域，例如阵风、磁干扰和能见度下降。该研究方法检查了概率估计器设计，该设计采用了一组可供使用的模型，其中包括标称惯性导航系统（INS）机械化方程、平台的动态约束、学习的未建模动力学以及具有不同随机噪声参数的模型。此外，还研究了视觉、惯性、超宽带、激光雷达和 GPS 等冗余传感器源的存在，以实现强大的导航系统性能，从而扩展平台的操作范围。 NSERC RTI 提案请求为“多传感器无人机导航研究测试台”提供设备支持，该测试台由配备定制视觉惯性激光雷达导航传感器包和 RTK GPS 系统的六轴飞行器无人机组成。所提出的设备对于使用运行期间的实际传感器数据反馈来完成多模型机器人导航研究成果的现场实验验证是必要的。该设备将使用最先进的软件和硬件工具，为总部提供有关无人机导航技术操作规模验证的实践培训。这些能力可以高度转移到加拿大各地的技术和研究部门，并显着增强 MUN ISL 的行业合作研究伙伴关系。