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、态势感知和移动的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。目前，无人机定位技术主要是全球导航卫星系统（GNSS）与高速率机载惯性测量单元（IMU）集成。不幸的是，GNSS/IMU技术由于卫星信号阻塞和IMU误差而在诸如密集的城市和室内区域的障碍环境中显著失败。这种限制目前阻止了无人机在复杂的障碍环境中执行低空任务。 为了填补这一空白，利用机载视觉/距离传感器进行精确定位和导航在过去十年中得到了广泛的研究。然而，有几个挑战阻碍了当前系统的安全采用和商业利用。一个主要的缺点是缺乏鲁棒性，由于当前的传感器融合方法的能力有限，以处理大量的异构传感器流与不同的高度非线性噪声特性在动态变化的环境中。另一个主要的缺点是昂贵的计算。基于视觉/测距的综合定位系统必须以极高的速率处理大量的数据扫描，以确保对无人机平台的稳定控制。 小型无人机面临更多挑战。它们真正的3D机动和悬停给估计方法和实时性能带来了一些挑战。此外，转子表现出的高振动和电磁干扰会导致难以建模的即时传感器误差。加速度计和陀螺仪在高振动下都会受到大噪声的影响，而磁力计在电磁干扰源附近无法正常工作。 基于申请人以前在多传感器定位系统方面的工作，该研究计划将专注于提高小型无人机平台在复杂障碍GNSS挑战/拒绝环境中的定位和导航能力。该研究将探索人工智能方法和模糊理论与传统信号处理和估计技术的集成，以开发适用于无人机动力学的新运动模型和新型非线性自适应传感器融合方法，可以处理异构低成本噪声传感器。板载实时处理将使用图形处理单元（GPU）处理器进行优化。研究成果可以很容易地应用于其他新兴技术，如自动驾驶汽车，自主机器人和增强现实。