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.

该提案旨在获得资金，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）在远程操作和自主设置中为多个工业，调查和研究部门提供服务。目前，这些应用程序采用GPS航点导航控制服务于客户要求的检查任务。然而，对于许多工业应用，在变化的环境条件下，在GPS接收不完整的现场执行结构检查任务、海上结构检查、船舶干船坞检查、操作规模无人机交付以及环境监测，以支持恶劣环境下的航行。这些应用需要多传感器和多模型方法，并具有强大的冗余措施，以实现平台的安全关键自主性。 该研究计划具有基础知识开发和操作规模验证，其中以一致的方式使用系统和传感器模型的存档来可靠地观察机器人的导航状态，即，空中机器人通过GPS限制区域、通过预先绘制的区域以及具有变化的环境条件（例如阵风、磁干扰和能见度降低）的区域过渡。研究方法探讨概率估计器的设计，采用了一个银行的模型在其处置，包括标称惯性导航系统（INS）机械化方程，动态约束的平台，学习未建模的动态，和模型与不同的随机噪声参数。此外，存在的冗余传感器源，从视觉，惯性，超宽带，激光雷达，和GPS的研究，以实现强大的导航系统的性能，扩展平台的操作包线。该NSERC RTI提案要求为“多传感器无人机导航研究试验台”提供设备支持，该试验台包括配备定制视觉惯性激光雷达导航传感器包和RTK GPS系统的六旋翼无人机。建议的设备是必要的，以完成现场实验验证的多模型机器人导航的研究成果，在操作过程中使用实际的传感器数据反馈。该设备将使用最先进的软件和硬件工具为HQP提供有关无人机导航技术操作规模验证的实践培训。这些能力可高度转移到加拿大的技术和研究部门，并显着增强MUN ISL的行业合作研究伙伴关系。