Autonomous dynamic docking of UAV based on UWB-vision in GPS-denied environment

Cheng, Cheng; Li, Xiuxian; Xie, Lihua; Li, Li

doi:10.1016/j.jfranklin.2022.03.005

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…Compared with relative positioning based on visual search, the method here has wider-range measurements, shorter searching times and requires fewer computing resources. Compared with [29,30], this study considers a dynamic random target. Compared with [28], we consider the measurement error and use historical data for estimation in control.…”

Section: Remarkmentioning

confidence: 99%

“…Subsequently, the position estimation error, q := q − q, is defined, and the root mean square (RMS) and standard deviation (SD) of the positioning error are made for the navigation and landing phases, respectively. In order to compare the superiority of MIFG, we perform a comparison with the forgetting factor least squares method [30] ((FFLS) where the forgetting factor is taken as 0.9) in the navigation phase, as shown in Tables 1 and 2. Through the analysis of RMS and SD, it can be seen that, during the navigation phase, both algorithms can estimate relative position data that meet the task of imprecise navigation.…”

Section: Simulationmentioning

confidence: 99%

“…A dynamic docking scheme based on odometry and UWB measurements is proposed to overcome the field of view limitation and save computational costs [28,29]. Furthermore, considering a docking target without an odometer, an autonomous docking scheme based on UWB and vision with a uniform linear motion platform is proposed [30]. In these applications, docking is inefficient, and parties either have absolute positioning devices or conduct a large area of searches, which largely causes unnecessary deployment or computing costs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Unmanned Aerial Vehicle (UAV)/Unmanned Ground Vehicle (UGV) Dynamic Autonomous Docking Scheme in GPS-Denied Environments

Cheng,

Li,

Xie

et al. 2023

Drones

Self Cite

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This study designs a navigation and landing scheme for an unmanned aerial vehicle (UAV) to autonomously land on an arbitrarily moving unmanned ground vehicle (UGV) in GPS-denied environments based on vision, ultra-wideband (UWB) and system information. In the approaching phase, an effective multi-innovation forgetting gradient (MIFG) algorithm is proposed to estimate the position of the UAV relative to the target using historical data (estimated distance and relative displacement measurements). Using these estimates, a saturated proportional navigation controller is developed, by which the UAV can approach the target, making the UGV enter the field of view (FOV) of the camera deployed in the UAV. Then, a sensor fusion estimation algorithm based on an extended Kalman filter (EKF) is proposed to achieve accurate landing. Finally, a numerical example and a real experiment are used to support the theoretical results.

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Section: Remarkmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Unmanned Aerial Vehicle (UAV)/Unmanned Ground Vehicle (UGV) Dynamic Autonomous Docking Scheme in GPS-Denied Environments

Cheng,

Li,

Xie

et al. 2023

Drones

Self Cite

View full text Add to dashboard Cite

show abstract

“…Machine vision positioning plays a crucial role in the autonomous flight and docking process, improving their positioning accuracy and safety. Cheng et al [19] proposed a method that combines ultra-wideband and visual positioning, and successfully guided UAVs to land in the target area without GPS. Ma et al [20] proposed a ground stereo vision guidance method that simulates human vision when studying UAV landing without GPS.…”

Section: Introductionmentioning

confidence: 99%

Visual guidance technology of flying cars based on multilevel markers and depth

Chen,

SHAO,

Zheng

2023

Preprint

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Split-type flying car will play an important role in the future transportation. This paper adopts a guidance method that couples visual information and depth information, and improves the docking accuracy through the mutual cooperation of the drone and the vehicle. Firstly, a multi-level docking marker is designed to achieve adaptive target matching within different distances during the docking process. The marker has strong robustness and can adapt to complex scenes such as occlusion, strong light, and large angle tilting, providing the redundant corner points required for machine vision detection pose information accurately. Secondly, a three-dimensional pose estimation algorithm is proposed, which can introduce depth information to correct the homography matrix. The algorithm combines the advantages of strong robustness to multi-level marker detection and high accuracy of depth information, and can output millimeter-level precision pose information in different environments, different inclination angles, and different occlusions. Finally, a flying car model experiment was carried out, and the results showed that the guidance technology can obtain millimeter-level precise pose information during the entire process of long distance-near distance-completion of docking, thus realizing precise docking.

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“…A large amount of work has been conducted on UWB and vision-guided landing in recent years. A UWB-vision combined autonomous landing framework [15][16][17] was developed that uses UWB technology to provide relative localization and vision localization to guide the final precision landing, but it is hard to provide high-frequency and real-time localization information due to the low update rate of the UWB system. To improve reliability and consistency, a UWB-IMU fusion relative location algorithm was implemented in autonomous landing [18]; however, without guided vision in the final descending stage, the landing precision is hard to guarantee.…”

Section: Introductionmentioning

confidence: 99%

An Integrated UWB-IMU-Vision Framework for Autonomous Approaching and Landing of UAVs

et al. 2022

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Unmanned Aerial Vehicles (UAVs) autonomous approaching and landing on mobile platforms always play an important role in various application scenarios. Such a complicated autonomous task requires an integrated multi-sensor system to guarantee environmental adaptability in contrast to using each sensor individually. Multi-sensor fusion perception demonstrates great feasibility to compensate for adverse visual events, undesired vibrations of inertia sensors, and satellite positioning loss. In this paper, a UAV autonomous landing scheme based on multi-sensor fusion is proposed. In particular, Ultra Wide-Band (UWB) sensor, Inertial Measurement Unit (IMU), and vision feedback are integrated to guide the UAV to approach and land on a moving object. In the approaching stage, a UWB-IMU-based sensor fusion algorithm is proposed to provide relative position estimation of vehicles with real time and high consistency. Such a sensor integration addresses the open challenge of inaccurate satellite positioning when the UAV is near the ground. It can also be extended to satellite-denied environmental applications. When the landing platform is detected by the onboard camera, the UAV performs autonomous landing. In the landing stage, the vision sensor is involved. With the visual feedback, a deep-learning-based detector and local pose estimator are enabled when the UAV approaches the landing platform. To validate the feasibility of the proposed autonomous landing scheme, both simulation and real-world experiments in extensive scenes are performed. As a result, the proposed landing scheme can land successfully with adequate accuracy in most common scenarios.

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Autonomous dynamic docking of UAV based on UWB-vision in GPS-denied environment

Cited by 9 publications

References 18 publications

A Unmanned Aerial Vehicle (UAV)/Unmanned Ground Vehicle (UGV) Dynamic Autonomous Docking Scheme in GPS-Denied Environments

A Unmanned Aerial Vehicle (UAV)/Unmanned Ground Vehicle (UGV) Dynamic Autonomous Docking Scheme in GPS-Denied Environments

Visual guidance technology of flying cars based on multilevel markers and depth

An Integrated UWB-IMU-Vision Framework for Autonomous Approaching and Landing of UAVs

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