Unmanned Aerial Vehicles(UAVs)are promising for their agile flight capabilities,allowing them to carry out tasks in various complex scenarios.The efficiency and accuracy of UAV operations significantly depend on high-...Unmanned Aerial Vehicles(UAVs)are promising for their agile flight capabilities,allowing them to carry out tasks in various complex scenarios.The efficiency and accuracy of UAV operations significantly depend on high-precision positioning technology.However,the existing positioning techniques often struggle to achieve accurate position estimates in conditions of Non-Line-Of-Sight(NLOS).To address this challenge,we propose a novel high-precision UAV positioning method based on MultiLayer Perceptron(MLP)integrating Ultra-WideBand(UWB)and Inertial Measurement Unit(IMU)technologies,which can acquire centimeter-level high-precision location estimation.In the method,we simultaneously extract key features from channel impulse responses and state space of UAV for training an MLP model,which can not only reduce error of UWB signals from dynamically flying UAV to anchor in NLOS environments,but also adapt to the diverse environment settings.Specifically,we respectively apply the anchor node assisted position calibration method and cooperative positioning techniques to the dynamic flying UAVs for solving the issues of UWB signal being blocked and lost.We conduct extensive real-world experiments to demonstrate the effectiveness of our approach.The results show that the median positioning errors of UAV in hovering and flight are 6.3 cm and within 20 cm,respectively.展开更多
The research of unmanned aerial vehicles＇（UAVs＇）autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points ...The research of unmanned aerial vehicles＇（UAVs＇）autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points cannot be obtained accurately,and the pose estimation algorithms based on the feature points have low precision.In this research,the pose estimation algorithm of UAV is proposed based on feature lines of the cooperative object for autonomous landing.This method uses the actual shape of the cooperative-target on ground and the principle of vanishing line.Roll angle is calculated from the vanishing line.Yaw angle is calculated from the location of the target in the image.Finally,the remaining extrinsic parameters are calculated by the coordinates transformation.Experimental results show that the pose estimation algorithm based on line feature has a higher precision and is more reliable than the pose estimation algorithm based on points feature.Moreover,the error of the algorithm we proposed is small enough when the UAV is near to the landing strip,and it can meet the basic requirements of UAV＇s autonomous landing.展开更多
When a cluster of unmanned aerial vehicles (UAVs) is flying in formation, it is crucial to maintain the formation and not to be interfered by external electromagnetic wave signals. In order to maintain the formation, ...When a cluster of unmanned aerial vehicles (UAVs) is flying in formation, it is crucial to maintain the formation and not to be interfered by external electromagnetic wave signals. In order to maintain the formation, this paper proposes to use pure azimuth passive positioning to adjust the position of UAVs, i.e., certain UAVs in the formation transmit signals, the rest of the UAVs receive the signals passively, and extract the orientation information from them to adjust the position of the UAVs [1] [2] [3]. In this paper, the position adjustment problem of UAVs in “circular” formation flight under three models is investigated. To address the problem of “how to obtain the position of the receiving UAV when there are two UAVs with known numbers and evenly distributed on the circumference in addition to the UAV transmitting at the known center of the circle, and the rest of the UAVs with slight deviations in their positions are receiving the signals”, two purely mathematical geometric methods, namely, triangular localization method and polar co-ordinate method, are proposed respectively. We have determined the position of the receiving UAV;we have used the exhaustive method and the construction and disproof method to solve the problem of “how many UAVs are needed to transmit signals in order to realize the effective positioning of the UAVs when it is known that a certain UAV with a slight deviation in its position receives the signals emitted by two UAVs at the same time”, and the results show that: in addition to the known signals emitted by two UAVs, it is also necessary to transmit the signals emitted by two UAVs. The results show that in addition to the known two UAVs transmitting signals, two additional UAVs are required to transmit signals for precise po-sitioning. When the position of UAVs has deviation at the initial moment, the ideal approximation method and the target delimitation method are pro-posed, and the target of nine UAVs uniformly distributed on a circle of a spe-cific radius is achieved through several adjustments, after which the ad-vantages and disadvantages of each model are analyzed, and suggestions for improvement are put forward. The purely azimuthal passive localization method and the constructed model approach proposed in this paper can be extended to other fields, such as spacecraft formations in space and battle-ship formations at sea, as well as other formation flight position adjustment problems.展开更多
With the increasing application of UAVs,UAV positioning technology for indoor complex environment has become a hot research issue in the industry.The traditional UWB positioning technology is affected by problems such...With the increasing application of UAVs,UAV positioning technology for indoor complex environment has become a hot research issue in the industry.The traditional UWB positioning technology is affected by problems such as multipath effect and non-line-of-sight propagation,and its application in complex indoor environments has problemssuch as poor positioning accuracy and strong noise interference.We propose an improved LSE-EKF optimisation algorithm for UWB positioning in indoor complex environments,which optimises the initial measurement data through a BP neural network correction model,then optimises the coordinate error using least squares estimation to find the best pre-located coordinates,finally eliminates the interference noise in the pre-located coordinate signal through an EKF algorithm.It has been verified by experiments that the evaluation index can be improved by more than 9%compared with EKF algorithm data,especially under non-line-of-sight(NLOS)conditions,which enhances the possibility of industrial application of indoor UAV.展开更多
基金supported in part by the National Natural Science Foundation of China(No.62202054)the China Postdoctoral Science Foundation(No.2024M750199)the Young Elite Scientists Sponsorship Program by Chinese Association for Science and Technology(No.2023QNRC001).
文摘Unmanned Aerial Vehicles(UAVs)are promising for their agile flight capabilities,allowing them to carry out tasks in various complex scenarios.The efficiency and accuracy of UAV operations significantly depend on high-precision positioning technology.However,the existing positioning techniques often struggle to achieve accurate position estimates in conditions of Non-Line-Of-Sight(NLOS).To address this challenge,we propose a novel high-precision UAV positioning method based on MultiLayer Perceptron(MLP)integrating Ultra-WideBand(UWB)and Inertial Measurement Unit(IMU)technologies,which can acquire centimeter-level high-precision location estimation.In the method,we simultaneously extract key features from channel impulse responses and state space of UAV for training an MLP model,which can not only reduce error of UWB signals from dynamically flying UAV to anchor in NLOS environments,but also adapt to the diverse environment settings.Specifically,we respectively apply the anchor node assisted position calibration method and cooperative positioning techniques to the dynamic flying UAVs for solving the issues of UWB signal being blocked and lost.We conduct extensive real-world experiments to demonstrate the effectiveness of our approach.The results show that the median positioning errors of UAV in hovering and flight are 6.3 cm and within 20 cm,respectively.
基金supported by the NUAA Fundamental Research Funds(No.NS2013034)
文摘The research of unmanned aerial vehicles＇（UAVs＇）autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points cannot be obtained accurately,and the pose estimation algorithms based on the feature points have low precision.In this research,the pose estimation algorithm of UAV is proposed based on feature lines of the cooperative object for autonomous landing.This method uses the actual shape of the cooperative-target on ground and the principle of vanishing line.Roll angle is calculated from the vanishing line.Yaw angle is calculated from the location of the target in the image.Finally,the remaining extrinsic parameters are calculated by the coordinates transformation.Experimental results show that the pose estimation algorithm based on line feature has a higher precision and is more reliable than the pose estimation algorithm based on points feature.Moreover,the error of the algorithm we proposed is small enough when the UAV is near to the landing strip,and it can meet the basic requirements of UAV＇s autonomous landing.
文摘When a cluster of unmanned aerial vehicles (UAVs) is flying in formation, it is crucial to maintain the formation and not to be interfered by external electromagnetic wave signals. In order to maintain the formation, this paper proposes to use pure azimuth passive positioning to adjust the position of UAVs, i.e., certain UAVs in the formation transmit signals, the rest of the UAVs receive the signals passively, and extract the orientation information from them to adjust the position of the UAVs [1] [2] [3]. In this paper, the position adjustment problem of UAVs in “circular” formation flight under three models is investigated. To address the problem of “how to obtain the position of the receiving UAV when there are two UAVs with known numbers and evenly distributed on the circumference in addition to the UAV transmitting at the known center of the circle, and the rest of the UAVs with slight deviations in their positions are receiving the signals”, two purely mathematical geometric methods, namely, triangular localization method and polar co-ordinate method, are proposed respectively. We have determined the position of the receiving UAV;we have used the exhaustive method and the construction and disproof method to solve the problem of “how many UAVs are needed to transmit signals in order to realize the effective positioning of the UAVs when it is known that a certain UAV with a slight deviation in its position receives the signals emitted by two UAVs at the same time”, and the results show that: in addition to the known signals emitted by two UAVs, it is also necessary to transmit the signals emitted by two UAVs. The results show that in addition to the known two UAVs transmitting signals, two additional UAVs are required to transmit signals for precise po-sitioning. When the position of UAVs has deviation at the initial moment, the ideal approximation method and the target delimitation method are pro-posed, and the target of nine UAVs uniformly distributed on a circle of a spe-cific radius is achieved through several adjustments, after which the ad-vantages and disadvantages of each model are analyzed, and suggestions for improvement are put forward. The purely azimuthal passive localization method and the constructed model approach proposed in this paper can be extended to other fields, such as spacecraft formations in space and battle-ship formations at sea, as well as other formation flight position adjustment problems.
基金supported by Beijing University of Chemical Technology[0103/21570118000].
文摘With the increasing application of UAVs,UAV positioning technology for indoor complex environment has become a hot research issue in the industry.The traditional UWB positioning technology is affected by problems such as multipath effect and non-line-of-sight propagation,and its application in complex indoor environments has problemssuch as poor positioning accuracy and strong noise interference.We propose an improved LSE-EKF optimisation algorithm for UWB positioning in indoor complex environments,which optimises the initial measurement data through a BP neural network correction model,then optimises the coordinate error using least squares estimation to find the best pre-located coordinates,finally eliminates the interference noise in the pre-located coordinate signal through an EKF algorithm.It has been verified by experiments that the evaluation index can be improved by more than 9%compared with EKF algorithm data,especially under non-line-of-sight(NLOS)conditions,which enhances the possibility of industrial application of indoor UAV.
