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: 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=683223
• 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.

无人驾驶汽车（UAV）越来越多地用于遥感，情况意识和移动映射的广泛应用中。他们有能力携带传感器，处理器，发射机和成像设备，并且具有独特的全向操作特性。无人机的安全操作需要准确的定位和导航系统。目前的无人机定位技术由全球导航卫星系统（GNSS）主导，该系统与高速载惯性测量单元（IMU）集成在一起。不幸的是，由于卫星信号阻塞和IMU错误，GNSS/IMU技术在狭窄的城市和室内地区等受阻环境中大大失败。当前，该限制可防止无人机在复杂的阻塞环境中执行低空任务。******为了填补这一空白，在过去的十年中，已经进行了广泛的研究。但是，存在一些挑战，可以防止当前系统安全地采用和商业利用。一个主要的缺点是由于当前传感器融合方法能够处理具有不同高度非线性噪声特征在动态变化的环境中的大量异质传感器流的能力有限，因此缺乏鲁棒性。另一个主要缺点是昂贵的计算。基于视觉/范围的集成定位系统必须以极高的速率处理大量数据扫描，以确保对无人机平台的稳定控制。******小规模无人机面临其他挑战。他们真正的3D操纵和悬停在估计方法和实时性能上构成了一些挑战。另外，转子暴露的高振动和电磁干扰会导致很难建模的直接传感器误差。在高振动下，加速度计和陀螺仪都有大噪音，并且磁力计在与电子干扰源附近的近端不正常。该研究将探讨人工智能方法和模糊理论与常规信号处理和估算技术的整合，以开发适合无人机动力学和新型非线性自适应传感器融合方法的新运动模型，这些方法可以处理可以处理异质低速噪声传感器的异质。板载实时处理将使用图形处理单位“ GPU”处理器进行优化。研究结果可以很容易地应用于其他新兴技术，例如自动驾驶汽车，自动机器人和增强现实。