Latest Patents & Research on UAV Gimbal Tracking System

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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