An auto-landing strategy based on pan-tilt based visual servoing for unmanned aerial vehicle in GNSS-denied environments

Chen, Chengbin; Chen, Sifan; Hu, Guangsheng; Chen, Baihe; Chen, Pingping; Su, Kaixiong

doi:10.1016/j.ast.2021.106891

Cited by 33 publications

(19 citation statements)

References 40 publications

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“…To get the altitude information, a sonar sensor is usually used [22]. In speed control approach such as what is used in ArduPilot [23] or what is developed by the authors in [24] [25], the goal is to preserve a constant downhill speed for the UAV until reaching the ground level.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…To show the efficiency of the proposed technique with the state-of-the-art, we implemented different control algorithms for both stabilization and landing parts. For the landing part we implemented the pure PID control algorithm for UAVs in [24] and [25], called as L-PID in this paper, and for the stabilization algorithm we implemented a Gain-scheduled PID proposed in [16] and a fuzzy control proposed in [20] (called S-GPID and S-FLC respectively). Our proposed control algorithm in the comparison is shown by S-ours and L-ours for stabilization and landing respectively.…”

Section: ) Stabilization and Landing Processmentioning

confidence: 99%

“…[24] + S-GPID[16] L-PID[24] + S-FLC[20] L-PID[24] + S-ours L-ours + S-GPID[16] L-ours + S-FLC[20] L-ours + S-ours…”

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confidence: 99%

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An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

et al. 2022

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In this paper, an adaptable fuzzy control mechanism for an Unmanned Aerial Vehicle (UAV) to manipulate its mechanical actuators is provided. The mission (landing) for the UAV is defined to track (land on) an object that is detected by a deep learning object detection algorithm. The inputs of the controller are the location and speed of the UAV that has been calculated based on the location of the detected object.Two separate fuzzy controllers are proposed to control the UAV motor throttle and its roll and pitch over the mission and landing time. Fuzzy logic controller (FLC) is an intelligent controller that can be used to compensate for the non-linearity behaviour of the UAV by designing a specific fuzzy rule base . These rules will be utilized to adjust the control parameters during the mission and landing period in runtime.To add the effect of the ground for tuning the FLC membership function over the landing operation, a computational flow dynamic (CFD) modeling has been investigated. The proposed techniques is evaluated on MATLAB/Simulink simulation platform and real environment. Statistical analysis of the UAV location reported during stabilization and landing process, on both simulation and real platform, shows that the proposed technique outperforms the similar state-of-art control techniques for both mission and landing control.

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Section: Related Work and Motivationmentioning

confidence: 99%

Section: ) Stabilization and Landing Processmentioning

confidence: 99%

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An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

et al. 2022

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“…In the field of visual landing, Chen et al proposed a rapid autonomous landing strategy in GNSS-denied environments using the visual system. rough the status of the airborne camera and image information, the relative position information between the UAV and landing point can be obtained for navigation [25]. In 2021, Safe Landing Zones (SLZ) in crowded scenarios were proposed.…”

Section: Visual Navigation Instruments In Landingmentioning

confidence: 99%

Visual Landing Based on the Human Depth Perception in Limited Visibility and Failure of Avionic Systems

Mobini

Sabzehparvar

2022

Computational Intelligence and Neuroscience

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This paper introduces a novel visual landing system applicable to the accurate landing of commercial aircraft utilizing human depth perception algorithms, named a 3D Model Landing System (3DMLS). The 3DMLS uses a simulation environment for visual landing in the failure of navigation aids/avionics, adverse weather conditions, and limited visibility. To simulate the approach path and surrounding area, the 3DMLS implements both the inertial measurement unit (IMU) and the digital elevation model (DEM). While the aircraft is in the instrument landing system (ILS) range, the 3DMLS simulates more details of the environment in addition to implementing the DOF depth perception algorithm to provide a clear visual landing path. This path is displayed on a multifunction display in the cockpit for pilots. As the pilot’s eye concentrates mostly on the runway location and touch-down point, “the runway” becomes the center of focus in the environment simulation. To display and evaluate the performance of the 3DMLS and depth perception, a landing auto test is also designed and implemented to guide the aircraft along the runway. The flight path is derived simultaneously by comparison of the current aircraft and the runway position. The Unity and MATLAB software are adopted to model the 3DMLS. The accuracy and the quality of the simulated environment in terms of resolution, the field of view, frame per second, and latency are confirmed based on FSTD’s visual requirements. Finally, the saliency map toolbox shows that the depth of field (DOF) implementation increases the pilot’s concentration resulting in safe landing guidance.

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“…Accordingly, PBVS has been widely researched since it provides 3D pose estimation, a broad field of view, and higher accuracy for motion control. Nevertheless, this method has its own disadvantages, such as sensitivity to camera calibration and a need for accurate robot and camera models [14]. Moreover, the main shortcoming of existing vision-based control techniques is that they still do not consider the effects of robot dynamics in stability proofs.…”

mentioning

confidence: 99%

Task Space Control of Robot Manipulators based on Visual SLAM

Hashemi¹,

Mattila²

2023

Preprint

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This paper aims to address the open problem of designing a globally stable vision-based controller for robot manipulators. Accordingly, based on a hybrid mechanism, this paper proposes a novel task-space control law attained by taking the gradient of a potential function in SE(3). The key idea is to employ the Visual Simultaneous Localization and Mapping (VSLAM) algorithm to estimate a robot pose. The estimated robot pose is then used in the proposed hybrid controller as feedback information. Invoking Barbalat's lemma and Lyapunov's stability theorem, it is guaranteed that the resulting closed-loop system is globally asymptotically stable, which is the main accomplishment of the proposed structure. Simulation studies are conducted on a six degrees of freedom (6-DOF) robot manipulator to demonstrate the effectiveness and validate the performance of the proposed VSLAM-based control scheme.

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An auto-landing strategy based on pan-tilt based visual servoing for unmanned aerial vehicle in GNSS-denied environments

Cited by 33 publications

References 40 publications

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

Visual Landing Based on the Human Depth Perception in Limited Visibility and Failure of Avionic Systems

Task Space Control of Robot Manipulators based on Visual SLAM

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