Targeted Tracking System for Gimbals in UAV

Method for accurately composing images of moving targets using a drone with a gimbal and camera. The method involves determining the drone's heading angle and gimbal pitch angles based on the target's position in the camera frame. The drone flies to the target's position and the gimbal orients to center the target. This ensures the target appears in the desired location. The shooting parameters like heading and pitch angles, and target position are adjusted based on the target size and selected shooting mode.

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Operating unmanned aerial vehicles (UAVs) to navigate aggressively at high speeds, low altitudes, and in low-light conditions. The technique involves using two high-definition variable navigation cameras mounted on separate gimbals. The cameras are selectively pointed at fixation points in the environment. The UAV navigates based on images from both cameras. This adaptive foveal vision with selective fixation allows high acuity tracking of points during aggressive maneuvers. Fixed-imaging systems with low-resolution sensors provide peripheral vision. The variable cameras sample images at motion-dependent rates to reduce power.

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Visual tracking system that enables continuous tracking of a target object across multiple camera views by integrating a pan-tilt camera with a chassis that follows the object. The system extracts image frames, tracks the object, and generates motion parameters to control the chassis, while also performing rotational scans to maintain object visibility.

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A gimbal for photographing devices that integrates target tracking functionality directly into the gimbal, eliminating the need for mobile apps and enabling tracking of targets with various types of photographing devices. The gimbal features a vision module, wireless positioning module, and fill-in light module, which work together with the gimbal's motors to track and stabilize targets. The gimbal's control system continuously monitors the tracking process and performs position adjustments as needed to maintain accurate and stable tracking.

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Unmanned aerial vehicle (UAV) with improved vision and thermal management systems, as well as a removable gimbal module that allows interchangeability of cameras and improves image capture capabilities. The UAV has a body with arms, propellers, and canopy. The vision system isolates image capture devices from the body and chassis to prevent vibrations. The thermal management uses an intake blower adjacent to the intake port, unobstructed airflow, and a heatsink with internal cooling arrays to improve cooling efficiency. The gimbal module extends forward from the UAV and can be removed for swapping cameras. This allows vertical image capture and prevents obstruction of intake airflow.

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A tracking control method for tilt-rotor multi-rotor UAVs based on dual quaternions, which enables stable position and attitude tracking of the UAV by utilizing the compact and singularity-free representation of dual quaternions to model the UAV's motion. The method employs a dual quaternion-based tracking control law that combines proportional, integral, and derivative terms to achieve precise tracking of the desired configuration.

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Target tracking system for drone combat systems that automatically selects and tracks the closest target among multiple detected targets. The system employs a camera-based detection unit that analyzes images captured by the drone's optical sensor, identifies potential targets, and prioritizes them based on predefined criteria such as class, detection priority, and terrain characteristics. The system then automatically selects and tracks the closest target to the drone, automatically adjusting its position to maintain optimal tracking performance.

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Method for dynamic target estimation of unmanned aerial vehicles in information denial environments, comprising: (1) target filtering estimation using AOA measurements from two stationary markers with known positions, and (2) unmanned aerial vehicle path optimization to improve estimation accuracy and prevent collision.

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Method, device, equipment, and storage medium for automatic target tracking of unmanned aerial vehicle (UAV) gimbals. The method uses a particle filter to track a target area in the gimbal's field of view, determining the target's position and calculating the necessary pan/tilt adjustments to maintain it at the image center. The device comprises modules for feature determination, particle initialization, prediction, and pan/tilt control.

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Tracking a target by using an unmanned aerial vehicle (UAV) without prior knowledge of the target's velocity and position is a critical and challenging task. This paper proposes a feasible approach that combines image-based visual servo (IBVS) with robust control. More specifically, a target detection algorithm is employed to acquire and calculate the pixel coordinates of the target's center. Simultaneously, the UAV utilizes a depth camera to measure the depth distance between the target and the UAV. Subsequently, the UAV directly computes flight control commands at the image pixel level through robust control, which comprises a force controller for position and a torque controller for attitude, separately. The UAV can continuously track the target through the aforementioned procedures. To validate the reliability of the proposed approach, a series of experiments and simulations are conducted.

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A gimbal for stabilizing and maneuvering cameras on drones that provides improved flexibility, viewing angles, and stability compared to conventional gimbals. The gimbal has an arc-shaped shaft arm that connects the camera to the drone body. The camera can slide along the arc and rotate around it. This allows the camera to pivot and tilt without blocking views since the shaft is non-parallel to the rotation axes. The arc shape provides an enclosure for components like rollers and springs to smooth and tighten rotation. It also allows the camera to slide and rotate within the enclosure. This improves flexibility, stability, and avoids obstructions compared to connecting multiple perpendicular axes.

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Object recognition, localization, and tracking play a role of primordial importance in computer vision applications. However, it is still an extremely difficult task, particularly in scenarios where objects are attended to using fast-moving UAVs that need to robustly operate in real time. Typically the performance of these vision-based systems is affected by motion blur and geometric distortions, to name but two issues. Gimbal systems are thus essential to compensate for motion blur and ensure visual streams are stable. In this work, we investigate the advantages of active tracking approaches using a three-degrees-of-freedom (DoF) gimbal system mounted on UAVs. A method that utilizes joint movement and visual information for actively tracking spherical and planar objects in real time is proposed. Tracking methodologies are tested and evaluated in two different realistic Gazebo simulation environments: the first on 3D positional tracking (sphere) and the second on tracking of 6D poses (planar fiducial markers). We show that active object tracking is advantageous for UAV applications, ... Read More

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An adaptive image-based visual servoing (IBVS) controller is designed for a quadrotor unmanned aerial vehicle (UAV) to achieve robust tracking for moving targets, under underactuation and tight coupling constraints of UAV kinematics. Specifically, image features are selected from perspective image moments of a planar target to obtain virtual feature dynamics regarding UAV kinematics and dynamics. By constructing an auxiliary variable, a translational velocity observer for moving target is constructed by using virtual image features. An IBVS tracking controller is designed without target geometric information by combining UAV and visual feature dynamics. Designed controller and observer make the UAV robustly reach desired height and track the moving target, despite uncertainty of target movement. The controller has asymptotical convergence performance, and the target velocity is observed according to Lyapunov stability analysis. Simulation and experimental results show that the proposed method has smoother and more accurate performance in motion tracking and target velocity prediction... Read More

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Unmanned aerial vehicles (UAVs) have attracted massive attention in many engineering and practical applications in the last years for their characteristics and operation flexibility. For the UAV system, suitable control systems are required to operate appropriately and efficiently. An emerging control technique is visual servoing utilizing the onboard camera systems for inspecting the UAV's environment and autonomously controlling the UAV's operation. Artificial intelligence (AI) techniques are widely deployed in the visual servoing of autonomous UAV applications. Despite the increasing research in the field of AI-based visual control of UAV systems, comprehensive review articles that showcase the general trends and future directions in this field of research are limited. This work comprehensively examines the application and advancements of AI-enhanced visual servoing in autonomous UAV systems, covering critical control tasks and offering insights into future research directions for enhancing performance and applicability which is limited in the current literature. The paper first r... Read More

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This paper addresses the problem of autonomous landing of UAV using position-based visual servoing (PBVS) approach. An autonomous landing strategy is proposed, utilizing marker detection with a downward-facing monocular camera, target pose estimation, and velocity command computation. Experiments are performed in both static and dynamic scenarios. The results validate the effectiveness of this approach, demonstrating successful UAV landing with an average accuracy of 0.0169 m, ensuring safety throughout the flight.

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This article presents a Visual Servoing Nonlinear Model Predictive Control (NMPC) scheme for autonomously tracking a moving target using multirotor Unmanned Aerial Vehicles (UAVs). The scheme is developed for surveillance and tracking of contour-based areas with evolving features. NMPC is used to manage input and state constraints, while additional barrier functions are incorporated in order to ensure system safety and optimal performance. The proposed control scheme is designed based on the extraction and implementation of the full dynamic model of the features describing the target and the state variables. Real-time simulations and experiments using a quadrotor UAV equipped with a camera demonstrate the effectiveness of the proposed strategy.

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This paper proposes a new geometric control method for homography-based visual servo control of Underactuated UAVs. In order to solve the application difficulties of geometric control in HBVS and explore a visual servo control technology that can be applied to aerial detection operations, this paper integrates the geometric control into the visual servoing framework and design a new homography-based geometric visual servoing controller. The outer loop is used as feedback information using the virtual homography matrix between the two images. The inner loop controls the orientation of the UAVs through geometric control. The stability of the proposed controller is proved based on Lyapunovs theory. The proposed method has better transient performance and dynamic performance than the conventional visual servo method. The excellent performance of the controller has been proven by a large number of experiments. In addition, the application of the controller on an unmanned aerial manipulator is demonstrated.

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An optical image sensor system for detecting and tracking objects in digital video streams, particularly in GPS-denied environments. The system separates each image into spatially distinct subframes, registers them independently based on object movement, and coherently averages them to improve detectability and tracking of stationary and moving objects. This approach enables day and night detection of dim objects, including stars and satellites, as well as terrestrial and airborne targets, without compromising performance in harsh environments.

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Nowadays, unmanned aerial vehicles (UAVs) are playing their potential and role in various fields due to their unique advantages, and many tasks require UAVs to have the ability to stably track ground targets. In this paper, we design a vision-based tracking system, using which a quadrotor UAV equipped with a fixed monocular camera can track a moving target on the ground. The visual scheme includes a lightweight YOLOv3-tiny detector and a KCF tracker to provide ground target perception. And the relative position estimation between UAV and target is obtained according to the line-of-sight angle calculation and the Kalman Filter method. As for the real-time approach of the quad rotor UAV to the target, we adopt a PID velocity controller to provide continuous tracking capability. Finally, the results of simulation and flight experiments demonstrate that the designed system can enable the quadrotor UAV to perform stable tracking of a moving target on the ground.

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Position control in UAVs has an important function in modern times UAVs. With the development of computer vision, UAV visual servo control became a research topic. In this paper, a visual servo control system based on color characteristic detection is proposed. By introducing the concept of the pixel of error and proportional control, the input of the PID controller can be adjusted to reach the desired sensitivity of the whole system. A simulation experiment is conducted in Simulink, and it shows that the visual servo control has achieved its goal of real-time position control of a UAV with a target object.

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Unmanned aerial vehicle (UAV) application in visual object tracking requires a gimbal to stabilize the camera in following the objects movement. External disturbance and gimbal stability are the issues in this application. This study aims to analyze disturbance effects and study the stress and modal analysis on UAV gimbal using flexible body concepts. The three-dimensional (3D) gimbal is modeled using the RecurDyn software consisting of 3 arms and a camera. Each of the arms is connected using a revolute joint and a rotational force to represent a motor. The considered disturbances are step, pulse, ramp, and sine wave input. The PID controller is used to stabilize the gimbal arm from the gravity of the camera and external disturbance. The result shows that the PID controller is robust to step, pulse, and ramp disturbance, but not to the sin wave disturbance. In addition, the second arm of the gimbal is the most stressed component and is prone to vibration.

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A method for autonomous landing of an unmanned aerial vehicle (UAV) on dynamic platforms, such as moving vehicles or ships, using a vision-based approach. The method employs a camera system with adjustable gimbal to continuously track a tilted target and a horizontal target, with the gimbal rotating to maintain the tilted target at the camera's center of view. The UAV's propulsion unit is controlled based on the camera's view of the targets, with the horizontal target serving as a reference for vertical alignment.

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In this paper, we propose 2D dynamic visual servoing (Dynamic IBVS), where a quadrotor UAV tries to track a moving target using a single facing-down perspective camera. As an application, we propose the tracking of a car-type vehicle. In this case, data related to the altitude and the lateral angles have no importance for the visual system. Indeed, to perform the tracking, we only need to know the longitudinal displacements (along the x and y axes) and the orientation along the z-axis. However, those data are necessary for the quadrotors guidance problem. Thanks to the concept of differential flatness, we demonstrate that if we manage to extract the displacements according to the three axes and the orientation according to the yaw angle (the vertical axis) of the quadrotor, we can control all the other variables of the system. For this, we consider a camera equipped with a vertical stabilizer that keeps it in a vertical position during its movement (a gimbaled camera). Other specialized sensors measure information regarding altitude and lateral angles. In the case of classic 2D visu... Read More

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A three-loop inertial stabilization system for cantilevered gimbal systems in laser communication applications, comprising a first loop that dampens resonances using inertial actuators, a second loop that tracks residual jitter, and a third loop that drives a fast steering mirror to maintain pointing stability. The system employs a Kalman state estimator, linear quadratic regulator, and state-space model to actively suppress jitter and maintain line-of-sight stability.

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Visual servoing is a control method that utilizes image feedback to control robot motion, and it has been widely applied in unmanned aerial vehicle (UAV) motion control. However, due to field-of-view (FOV) constraints, visual servoing still faces challenges, such as easy target loss and low control efficiency. To address these issues, visual servoing control for UAVs based on the deep reinforcement learning (DRL) method is proposed, which dynamically adjusts the servo gain in real time to avoid target loss and improve control efficiency. Firstly, a Markov model of visual servoing control for a UAV under field-of-view constraints is established, which consists ofquintuplet and considers the improvement of the control efficiency. Secondly, an improved deep Q-network (DQN) algorithm with a target network and experience replay is designed to solve the Markov model. In addition, two independent agents are designed to adjust the linear and angular velocity servo gains in order to enhance the control performance, respectively. In the simulation environment, the effectiveness of the proposed... Read More

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A method for tracking a target object using an unmanned aerial vehicle (UAV) that determines occlusion of the target object by analyzing multi-frame response peak values and tracking frame size changes. The method includes obtaining image data from the UAV, determining the target object's location in each frame, and calculating a difference coefficient between response peak values and tracking frame sizes. Based on these coefficients, the method determines the occlusion degree of the target object and sends control instructions to the UAV to adjust its flight status. When the target object is completely occluded, the method re-detects the target object in subsequent frames.

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Method and system for modeling poor-texture tunnels using a vision-lidar coupling on an unmanned aerial vehicle (UAV). The system integrates a depth camera and lidar for simultaneous localization and mapping (SLAM), leveraging the wide-range information of the lidar and local details of the depth camera to improve accuracy. The system fuses point cloud data, raster maps, and pose information using Bayesian fusion, and iteratively refines the map model through feature matching between successive frames. The system also employs positioning UAVs and auxiliary lighting to enhance data quality and accuracy.

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A gimbal system is a mechanical apparatus that offers multiple degrees of freedom motions. Conventional gimbal motions are roll, pitch, and yaw (3 axes) or pan tilt motions (2 axes) in angles. In applications, these gimbal systems, in conjunction with cameras and other sensors, are used in footage recording for airplanes, helicopters, and UAVs (unmanned air vehicles) or as a handheld device for pictures. These applications are aligned with image tracking, surveillance, or even target tracking and engagement in more industry or military aspects.

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In this paper, an enhanced visual servoing method is designed for a quadrotor unmanned aerial vehicle (UAV) based on virtual plane image moments, under underactuation and tight coupling constraints of UAV kinematics. Moreover, in order to make the UAV search visual targets autonomously in target vicinity during flight, a flexible flight system is developed with stages of take-off, target searching, and image-based visual servoing (IBVS). With dual-camera sensor configuration, the UAV system searches targets from given directions while making localization. A virtual image plane is constructed and image moments are adopted to decouple UAV lateral movement. For a non-horizonal target, homography is utilized to construct the target plane and transform it into a horizonal plane. Backstepping techniques are used to derive the nonlinear controller to realize the IBVS strategy. Stability analysis proves global asymptotic performance of the closed-loop system. Experimental verification shows feasibility of the overall flight system and effectiveness of the visual servoing controller.

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Visual servoing of Unmanned Aerial Vehicles (UAVs) has achieved satisfactory performance in fixed and planar motion targets. Due to highly coupled system dynamics and the sensitivity of the target image to aircraft attitude, the problem for chasing free-flying targets remains challenging. In this paper, a vision-based algorithm is designed for controlling an UAV while tracking an intruder flying arbitrarily in 3D space. Image-based visual servoing is used to design controllers that depend directly on errors in image plane. Specifically, a virtual camera approach is adopted to decouple the UAV dynamics by compensating the pitch and yaw, and an improved image error term is proposed to reduce the impact of the UAV rotation on error signals in the process of tracking, thus a simplified control design is achieved, and the stability of the visual servo system is guaranteed. Comparison and ablation experiments in both simulated and real environments are provided to verify the effectiveness of the proposed method.

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This study proposes an image-based visual servoing (IBVS) method based on a velocity observer for an unmanned aerial vehicle (UAV) for tracking a dynamic target in Global Positioning System (GPS)-denied environments. The proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target. A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is proposed. The depth model ensures image feature accuracy and image trajectory smoothness in rotating target tracking. The relative velocities of the UAV and the dynamic target are estimated using the proposed velocity observer. Thanks to the velocity observer, translational velocity measurements are not required, and the control chatter caused by noise-containing measurements is mitigated. An integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the anti-disturbanc... Read More

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Using Unmanned Aerial Vehicles (UAVs) to perform high-altitude manipulation tasks beyond just passive visual application can reduce the time, cost, and risk of human workers. Prior research on aerial manipulation has relied on either ground truth state estimate or GPS/total station with some Simultaneous Localization and Mapping (SLAM) algorithms, which may not be practical for many applications close to infrastructure with degraded GPS signal or featureless environments. Visual servo can avoid the need to estimate robot pose. Existing works on visual servo for aerial manipulation either address solely end-effector position control or rely on precise velocity measurement and pre-defined visual visual marker with known pattern. Furthermore, most of previous work used under-actuated UAVs, resulting in complicated mechanical and hence control design for the end-effector. This paper develops an image-based visual servo control strategy for bridge maintenance using a fully-actuated UAV. The main components are (1) a visual line detection and tracking system, (2) a hybrid impedance force a... Read More

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Visual servoing is a method to achieve precise positioning and motion control of objects by visual feedback, and it is widely applied in the fields of robotics and unmanned aerial vehicles (UAVs) in recent years. This paper presents a novel image-based visual servoing (IBVS) control method for UAVs based on fuzzy logic to effectively solve the problem under field of view constraint and improve the control efficiency. In this paper, a fuzzy logic of servo gain is designed for the control input of visual servoing, which solves the problem of feature loss in IBVS and improves the efficiency. Meanwhile, a deep computing method based on known data is proposed to solve the unknown depth of Jacobian matrix, which makes the control easier to converge. The effectiveness of the proposed method is verified by the simulation of a quadrotor UAV equipped with a monocular camera.

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The basic prerequisite of visual servoing control for a quadrotor unmanned aerial vehicle (UAV) is to ensure that the visual targets are always within the field of view (FOV) of the camera. In this paper, a new geometric control approach is proposed under FOV constraint for the quadrotor, in which the control barrier function (CBF) is used to constrain the states of the quadrotor to guarantee the visibility of visual targets. Specifically, the CBFs related to the quadrotor states are firstly constructed to satisfy the forward invariance of the safety set, and the states of the quadrotor are restricted within the safe area. Then, control inputs calculated from the geometric controller are refined by quadratic programming (QP) with minimal modification. Comparative simulation results show the remarkable performance of the proposed method in ensuring the visibility of visual targets of the quadrotor visual servoing.

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The evolution of technology in the field of unmanned aerial vehicles (UAVs) has led to the miniaturization of the equipment on board these aircraft. The most used equipment mounted on a UAV is still the video sensor, with the help of which the video image from the aerial vehicle can be processed for dedicated applications (facility inspection, surveying and mapping, operational oversight, traffic control, etc.). In this paper, we present the prototyping of a three-axis gimbal using the direct drive technology to replace the current two-axis gimbal payload equipping our Hirrus UAV. The results demonstrate notable improvements both mechanically, by excluding the gearing of the transmission belt (generating friction and elasticity in the system), and in terms of video resolution, maintaining quality especially at high zoom levels (over 5x).

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A method for tracking objects using an unmanned aerial vehicle (UAV) equipped with both infrared and visible cameras. The method combines the infrared and visible images to create a single image, identifies the target within the combined image, and generates control signals to track the target using the UAV's imaging device. The combined image is created by matching features extracted from the infrared and visible images, and the control signals are generated based on the target's position and the UAV's configuration.

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One of the key requirements in many UAV applications is the capability of precise positioning and pinpoint landing. Precise positioning is essential for maintaining task-advantageous positions of UAV relative to a ground object with minimum deviation, and is crucial for task actions, such as geo-referencing, image capturing, and payload delivery. Pinpoint landing helps assure successful task conclusion and machine safety. In this paper, we present a technical approach to achieve precise positioning, fixated hovering, and pinpoint landing by autonomous visual navigation. Different visual processing methods, such as fiducial detection, object tracking, and visual odometry, are combined and integrated with navigation control. A visual-based autonomous control system has been developed and field tested with successful performance. The system is capable of achieving high-precision positioning with visual navigation in the process of changing altitude. The average lateral deviation at 100-meter altitude by a drone product with better flight stability is only 0.25 meter.

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Search and tracking of non-cooperative mobile targets, maneuvering in an area monitored by a quadrotor UAV is investigated. In order to reach this aim, We propose a robust approach based on vision and sliding mode controller. The proposed strategy is an Image-Based Visual Servoing (IBVS) approach using targets visual data projected in a virtual camera. For an effective visual target searching, a circular search trajectory is followed, with a high altitude using the Camera Coverage Area (CCA). A Sliding Mode Controller (SMC) based on Reaching Law with a Power Rate (RLPR) is applied to ensure the QUAV control in the presence of external disturbances and measurement uncertainties. Simulation results are presented to assess the proposed strategy considering different scenarios.

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Method to improve the automatic control capability of unmanned aerial vehicles (UAVs) by decoupling the flight path and target tracking functions. The method involves generating a flight path based on a first target's position, and while the UAV flies that path, controlling the camera to always track a second target. This allows the UAV to simultaneously fly to the first target and keep the second target in view. It improves real-time control versatility compared to using the same target for both flight and tracking.

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Unmanned Aerial Vehicles (UAV) are used for many services in various fields. We propose a video transmission relay system in which a fixed-wing UAV is used as a relay station. In order to establish the video transmission link, it is necessary to accurately point the antenna onboard a fixed-wing UAV to both the target ground station and the UAV for photography while making the UAV turn over the designated area. In this study, we propose a new antenna pointing control system, which uses a 2-axis gimbal to greatly reduce the attitude motion of the UAV, and describe evaluation results of the performance of the antenna pointing control system.

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Method and pan/tilt head for tracking moving objects with a gimbal that allows flexible and versatile object following. The gimbal has multiple tracking modes with different numbers of motors to match the dimension of the object's movement. This allows users to select the mode that best fits the object's motion. For example, using just the pan axis for horizontal tracking, or all axes for full 3D tracking. The gimbal also converts images to a standard orientation for target recognition regardless of the gimbal's position. This enables consistent tracking even if the gimbal's optical axis doesn't align with the object's movement.

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An aircraft control method and system that enables high-precision control of aerial photography by integrating flight control and gimbal control. The method involves the gimbal control system (GCS) obtaining a yaw control instruction and attitude angle information from the flight control system (FCS), and then controlling the gimbal's yawing motion based on both inputs. The FCS, in turn, receives actual yaw information from the gimbal and adjusts the aircraft's yawing motion to match the gimbal's motion, ensuring consistent and smooth yawing control. This integrated control approach enables high-quality aerial photography and resolves video freezing issues during low-speed yawing operations.

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Pan/tilt system for unmanned aerial vehicles (UAVs) that enables autonomous capture of high-resolution images for Geographic Information System (GIS) applications. The system comprises a dual-axis pan/tilt head with two independent motorized turntables, each comprising a fixed turntable and a movable turntable connected by a universal joint. Electromagnets on the fixed turntables interact with permanent magnets on the movable turntables to enable precise and smooth motion control. The system is designed for integration with UAVs and features shock-absorbing structures to ensure stable operation during flight.

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Manipulator systems are necessary part for unmanned autonomous systems (UAS) to achieve task executions and hence, they have been widely adopted in mobile platforms, such as UAV, UGV together with visual feedback to perform different tasks. Due to the unknown movements of the base, it is difficult to achieve the accurate manipulator visual servoing control since the depth information is continuously changed. In order to achieve the manipulator visual servoing with moving base, two solutions are presented in this paper. Firstly, a support vector regression based method is developed to predict the horizontal movements of the base. Thus, the position of the end-effector can be adjusted accordingly so as to reduce the influence of moving base on the end-effector. Then, an observer is proposed to estimate the depth change in presence of disturbance acted on the camera velocity. With the estimated depth, a visual servoing controller is developed. Simulation and experimental results are presented to illustrate the performance of the proposed solutions.

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Abstract The task of tracking a moving target by the UAV has two major challenges: one is that the multi-angle and multi-scale changes caused by the targets movement make the target detection more difficult, the other is that the stability and accurate of target tracking are difficult to guarantee. In order to solve these problems, using monocular vision to get the image information of the target and YOLOv4 is used to detect multi-angle and multi-scale moving targets. Then based on the image visual servo control method, the PID controller is designed, and the deviation between the target pixel and the ideal pixel is used as the input, the output of the controller is the desired pixel speed of the UAV in image coordinate system. the tracking speed of the UAV under the machine system is solved according to the image Jacobian matrix, and a tracking strategy is designed to improve the tracking accurate and stability. In the process of target tracking, the difference between the x-axis and Y-axis coordinates of the target center point and the image center point is less than 30 pixels. Th... Read More

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A gimbal control method and apparatus for unmanned aerial vehicles (UAVs) that enables precise control of camera orientation based on image coordinates and camera parameters. The method acquires camera parameters, such as field of view and resolution, and user-selected image coordinates of a target object. It then calculates the required gimbal attitude to position the target object at a preset location in the captured image, enabling ideal framing and composition.

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A machine vision-based unmanned aerial vehicle (UAV) target tracking system that improves tracking accuracy by compensating for flight disturbances and time delays. The system integrates machine vision, flight control, and simulation modules to capture and analyze real-time flight data, including visual, positioning, and sensor information. The flight simulation module uses this data to simulate flight scenarios, estimate disturbances and delays, and adjust flight parameters to optimize tracking performance.

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A control method for a gimbal that dynamically adjusts its response speed based on the acceleration of its base, enabling smooth tracking of targets at varying speeds while maintaining camera stability. The method determines the base acceleration and adjusts the gimbal's velocity coefficient accordingly, allowing for optimal camera attitude adjustments in response to changing base motion.

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System and method for tracking a target in 3D space using a combination of image data and location data. The system determines a target's 3D location by combining 2D pixel coordinates from image data with 3D location data from a tracker, using a physical dimension of the target to resolve ambiguities. The system can also update the target's location using a Kalman filter, and can determine the target's location even when pixels associated with the target are not visible in the image data.

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Tracking moving objects using a device with a projector and a multi-axis gimbal. The device projects a reference image with a pattern onto a surface. When a moving object passes through the projection, it distorts the pattern. The device captures this deformed projection image and adjusts the gimbal to track the pattern distortion. Since the pattern is caused by the moving object, tracking it allows expanding the tracking range beyond the fixed camera view.

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