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Inspection UAV landing navigation system by fusing UWB information and height data
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With the increasing applications of unmanned aerial vehicles (UAVs) in industries, it has drawn dramatic interests that utilising UAVs for internal inspection of oil tanks and vessels in recent years. Due to the fact that there is no global positioning system (GPS) and limited prior knowledge about operational environment, it is an unsolved problem on how to get the precise position information without relying on prior knowledge. To solve this problem, a Vision Aided Self-Localisation Two-way Time of Flight (VASTWTOF) algorithm is proposed. In the proposed algorithm, ultra-wideband (UWB) based sensor nodes are utilised for UAVs to achieve precise localisation in GPS-denied environment and the vision-based approach is adopted for anchor self-localisation. The simulation results show that the proposed algorithm can greatly improve the localisation performance and could be used for UAVs’ precise localisation in GPS-denied environment, especially for the internal inspection of oil tanks and vessels.
Though Deep Learning CNN is mostly used in UAV power line-inspection system for the application of intelligent image recognition technology, can design image features easily and has strong adaptability to complex environments, but three problems deafly influence the actual results of application system such as insufficient image samples library, scarce labeling samples, and absent open-data source. To conquer these problems, CBR is proposed as a strategy for knowledge reasoning, which transform the similar case-space to a new situation for problem-solving, so the combination of RBR and CBR is expected to construct our flexible case- decision diagnosis system, which integrates efficient machine learning methods to give their full advantages to guarantee the good performance of the system for fault detection. The on spot experimental results indicates our system performs efficiently, assist people in decision-making and can find potential equipment faults.
In the survey of high slope perilous rock-mass in open-pit iron mine, the traditional unmanned aerial vehicle (UAV) can overcome the shortcomings of high difficulty and high risk factor in artificial geological exploration. However, it is very easy to be disturbed by the magnetic field of iron ore due to its orientation with magnetic compass, which makes the work unable to carry out normally. In order to solve the above problems, the UAV with dual-GPS equipped with laser radar can resist the magnetic field interference to a large extent and scan the high slope. By means of field experiment and point cloud data post-progressing, a set of application method of dual GPS multi-rotor UAV laser measurement technology was proposed for geological survey of high slope perilous rock under the interference of magnetic field. The preliminary conclusions are also obtained: the dual GPS combined orientation method makes the multi-rotor UAV not affected by the iron ore magnetic field and can fly normally according to the flight route. At the same time, the airborne LiDAR technology can realize the long-distance and fast acquisition of the valid terrain data of high-slope dangerous rock mass. Through the fuzzy clustering method (FCM), the joint system of outcrop rock-mass is automatically recognized and categorized based on point cloud data captured. The plane normal vector of fitting structural surface and structural surface occurrence can then be acquired. The normal vector of structural surface and the occurrence of structural surface can be obtained by substituting calculation. The occurrence of the slope and the shape of the structural surface was further drew into the stereographic projection map in order to determine the dominant joint group which affects rock slope stability.
A new method to estimate the aircraft heading angle, wind velocity, and airspeed bias error is proposed without relying on the magnetometer. A GPS, rate-gyro, pitot-static tube, and sideslip angle sensor are combined through a design of an extended Kalman filter based on a vectorial geometric relation among the ground, air, and wind velocity. The feasibility of the method is validated through a desktop simulation. The flight test is conducted utilizing the estimated yaw angle as a guidance variable to evaluate the performance of the method. The estimated wind information is compared to the automatic weather system nearby.
Electromagnetic wave is susceptible to the multipath effect, especially in complex indoor environment. As transmission of non-line of sight (NLOS), it leads to bigger measurement error, which makes positioning accuracy decline. In the paper, we aim to compensate the measurement error mainly affected by the NLOS to enhance the positioning accuracy. Conceptions of “virtual inertial point” and “environmental factor” are introduced, and moreover acceleration data of inertial measurement unit (IMU) are adopted. A novel and simple NLOS error compensation method for high accuracy indoor positioning system based UWB is proposed. Based on the principle of inertia, IMU can be used to fix motion trend of tag and to get coordinate of virtual inertial point. The measurement coordinate can be acquired by UWB system. We can calculate the distances between virtual inertial point and real measurement in same area, respectively under line of sight (LOS) and NLOS in advance. The ratio of two different distances can be defined as environmental factor, which is mainly used to reflect the influence of NLOS on positioning accuracy quantitatively relative to LOS in the paper. Environmental factor, as the weighted coefficient, is adopted to merge coordinates of the virtual inertial point and measurement to obtain the coordinate compensated under NLOS. It is a process of scaling measurement down to user-defined area for positioning accuracy requirement to reduce the impact of NLOS. At last, Kalman Filter is used to smooth the set of coordinate compensated under NLOS to further improve positioning accuracy. Experimental results show that our method can effectively compensate the NLOS error in the indoor positioning system based ultra-wide band (UWB) technology. The positioning accuracy is improved nearby 80% in NLOS area.
Fusion techniques are employed in pedestrian tracking to achieve more accurate and robust tracking systems. A common approach is to fuse Inertial Navigation System (INS), worn by a pedestrian, with a radio-based system to complement each other and mitigate their shortcomings. Despite the increased accuracy achieved in the state-of-the-art approaches, the deployment complexity and cost of these tracking systems remain a major bottleneck. In this paper, a novel INS and Ultra-wideband (UWB) fusion approach, which complements INS only with ranging measurements obtained from UWB anchors placed at known location, is proposed. An adaptive UWB ranging uncertainty model is proposed and incorporated in a Particle Filter fusion algorithm, which reduces errors of the UWB measurements and enhances positioning accuracy. The proposed approach achieves significant reduction of the deployment complexity and cost compared to other approaches that have comparable tracking performance. The pedestrian tracking system is implemented using the built-in inertial measurement unit of a smartphone and DecaWave TREK1000 UWB development kit. Two practical long-distance pedestrian tracking experiments are conducted to demonstrate the accuracy and robustness of the proposed approach, which reduces mean position error up to 73.23 % when compared to INS only tracking results.
Review on UAV intelligent technology for transmission
- X R Miao
- Z Y Liu
- Q C Yan
X. R. Miao, Z. Y. Liu, and Q. C. Yan. "Review on UAV intelligent technology for transmission." Journal of
Fuzhou University (Natural Science Edition), 2020, 48(02): 198-209.
UAV line inspection command system based on image recognition technology
- Peng
C. G. Peng. "UAV line inspection command system based on image recognition technology." Electronic Design
Engineering, 2021, 29(4): 53-56, 61.
Review on UAV intelligent technology for transmission
- Miao