Real-time Onboard Multi-sensor Navigation Systems for Unmanned Aerial Vehicles in GPS-challenging Environments

GPS 挑战环境中无人机的实时机载多传感器导航系统

• 批准号: RGPIN-2017-06261
• 负责人: Atia, Mohamed
• 金额: $ 2.11万
• 依托单位: Carleton University
• 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=712141
• 关键词: Real; time; Onboard; Multi; sensor

Unmanned Aerial Vehicles (UAVs) are being increasingly utilized in wide range of applications in remote sensing, situation awareness, and mobile mapping. They have the ability to carry sensors, processors, transmitters, and imaging equipment and they have unique omnidirectional maneuvering characteristics. Safe operation of UAVs requires accurate positioning and navigation systems. Present UAV positioning technology is dominated by Global Navigation Satellite Systems (GNSS) integrated with high-rate onboard inertial measurement units(IMU). Unfortunately, GNSS/IMU technology fails significantly in obstructed environments such as dense urban and indoor areas due to satellite signal blockage and IMU errors. This limitation currently prevents UAVs from being used to perform low-altitude tasks in complex obstructed environments. To fill this gap, utilization of onboard vision/range sensors for accurate positioning and navigation has been extensively studied in the last decade. However, there are several challenges that prevent current systems from being safely adopted and commercially utilized. One major drawback is lack of robustness due to the limited ability of current sensor fusion methods to handle massive streams of heterogeneous sensors with different highly nonlinear noise characteristics in a dynamically changing environment. Another major drawback is the expensive computation. Vision/range-based integrated positioning systems have to process massive scans of data at extremely high rates to assure stable control of UAV platforms. Small scale UAVs have additional challenges. Their truly 3D maneuvering and hovering impose several challenges on the estimation methods and real-time performance. In addition, the high vibration and electromagnetic interference exhibited by rotors cause immediate sensors errors that are difficult to model. Both accelerometers and gyroscopes suffer from large noise under high vibration and magnetometers do not work properly in close proximity to electromagnetic interference sources. Building on the applicant's previous work in multi-sensor positioning systems, this research program will focus on advancing the positioning and navigation capabilities of small scale UAV platforms in complex obstructed GNSS-challenging/denied environments. The research will explore the integration of artificial intelligence methods and fuzzy theory with conventional signal processing and estimation techniques to develop new motion models that are suitable for UAV dynamics and novel nonlinear adaptive sensor fusion methods that can handle heterogeneous low-cost noisy sensors. Onboard real-time processing will be optimized using Graphical-Processing-Unit “GPU”-enabled processors. The research outcomes can be readily applied in other emerging technologies such as self-driving cars, autonomous robots, and augmented reality.

无人机在遥感、态势感知和移动测绘等领域的应用越来越广泛。它们能够携带传感器、处理器、发射器和成像设备，并且具有独特的全向机动特性。无人机的安全运行需要精确的定位和导航系统。当前的无人机定位技术以全球导航卫星系统（GNSS）和高速率机载惯性测量单元（IMU）相结合为主导。不幸的是，由于卫星信号阻塞和IMU误差，GNSS/IMU技术在人口密集的城市和室内等受阻环境中明显失败。这一限制目前阻碍了无人机在复杂的受阻环境中执行低空任务。