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The use of unmanned aerial vehicles (UAVs) for military, scientific, and civilian sectors are increasing drastically in recent years. This study presents algorithms for the visual-servo control of an UAV, in which a quadrotor helicopter has been stabilized with visual information through the control loop. Unlike previous study that use pose estimation approach which is time consuming and subject to various errors, the visual-servo control is more reliable and fast. The method requires a camera on-board the vehicle, which is already available on various UAV systems. The UAV with a camera behaves like an eye-in-hand visual servoing system. In this study the controller was designed by using two different approaches; image based visual servo control method and hybrid visual servo control method. Various simulations are developed on Matlab, in which the quadrotor aerial vehicle has been visual-servo controlled. In order to show the effectiveness of the algorithms, experiments were performed on a model quadrotor UAV, which suggest successful performance.

This paper presents a robust and adaptive visual servoing-based landing control method for unmanned aerial vehicles (UAVs) equipped with a three-axis gimbal camera. To address the limitations of fixed-camera configurations, the proposed approach integrates pixel-level field-of-view (FOV) constraints and leverages the gimbal’s agility for enhanced visual tracking. The landing task is formulated as a constrained image-based control problem, where tracking errors of image features are rigorously bounded using prescribed performance functions. A velocity observer is incorporated to estimate the time-varying motion of the landing platform in real time, enabling accurate autonomous landing without relying on external communication or infrastructure. Lyapunov-based stability analysis confirms the theoretical soundness of the control strategy. Simulation results validate the effectiveness and robustness of the proposed method, demonstrating improved accuracy, adaptability, and practical applicability in UAV landing scenarios.

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.

This paper presents the development of a visual-perception system on a dual-arm mobile robot for human-robot interaction. This visual system integrates three subsystems. Hand gesture recognition is utilized to trigger human-robot interaction. Engagement and intention of the participants are detected and quantified through a cognitive system. Visual servoing uses YOLO to identify the object to be tracked and hybrid, model-based tracking to follow the object’s geometry. The proposed visual-perception system is implemented in the developed dual-arm mobile robot, and experiments are conducted to validate the proposed method’s effects on human-robot interaction applications.

Achieving precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms, such as Autonomous Surface Vehicles (ASVs), is challenging, particularly in GPS-denied environments with dynamic disturbances. Conventional methods often rely on high-level waypoint navigation, extensive manual tuning, and expensive sensors. In this work, we propose an adaptive Proportional-Integral-Derivative (PID) controller optimization using a Neural Network-Particle Swarm Optimization (NN-PSO) algorithm. The algorithm dynamically tunes the PID controller, significantly reducing manual tuning effort, while relying solely on a low-cost camera and altitude sensor. The NN-PSO algorithm allows the UAV to land with an average error of 5 cm on static platforms and 10 cm on moving boats, based on multiple test flights. Our method also increases the maximum landing speed to 80.9% of the UAV’s top flight speed, a considerable improvement over existing systems. Our approach not only optimizes landing precision but also introduces techniques for ensuring soft landings, reducing oscillations, and preventing target misses. These enhancements make the method robust across varying flight altitudes and ASV speeds. Furthermore, this approach is applicable to a variety of GPS-denied scenarios, including rescue missions, package deliveries, and workspace inspections, without requiring costly equipment or extensive parameter tuning. Field experiments confirm the precision and stability of the proposed system, validating its performance in real-world conditions. [Video]

Assistive robotic arms (ARAs) that provide care to the elderly and people with disabilities, are a significant part of Human-Robot Interaction (HRI). Presently available ARAs provide non-intuitive interfaces such as joysticks for control and thus, lacks the autonomy to perform daily activities. This study proposes that, for inducing autonomous behavior in ARAs, visual sensors integration is vital, and visual servoing in the direct Cartesian control mode is the preferred method. Generally, ARAs are designed in a configuration where its end-effector’s position is defined in the fixed base frame while orientation is expressed in the end-effector frame. We denoted this configuration as ‘mixed frame robotic arms’. Consequently, conventional visual servo controllers which operate in a single frame of reference are incompatible with mixed frame ARAs. Therefore, we propose a mixed-frame visual servo control framework for ARAs. Moreover, we enlightened the task space kinematics of a mixed frame ARAs, which led us to the development of a novel “mixed frame Jacobian matrix”. The proposed framework was validated on a mixed frame JACO-2 7 DoF ARA using an adaptive proportional derivative controller for achieving image-based visual servoing (IBVS), which showed a significant increase of 31% in the convergence rate, outperforming conventional IBVS joint controllers, especially in the outstretched arm positions and near the base frame. Our Results determine the need for the mixed frame controller for deploying visual servo control on modern ARAs, that can inherently cater to the robotic arm’s joint limits, singularities, and self-collision problems.

This paper is oriented to the transmission line inspection environment, aiming to solve the problem of how to control the robotic arm to quickly and accurately grasp the target object, and work on the planning of the motion path and the position-based visual servo control method. For the spatial trajectory planning of the robotic arm, linear trajectory planning and circular arc trajectory planning are carried out respectively. Between the reaction speed problem of the traditional RRT motion algorithm in collision-free motion planning, a two-way RRT-Connect path planning method is proposed. Then the visual servo control algorithm of the UAV robotic arm is proposed. The position-based visual servo control method is adopted, while the EK-SVSF position estimation algorithm and the OWA-based stepwise data fusion algorithm are combined for optimal position estimation of the target during transmission line inspection. It is verified in simulation experiments that the improved RRT algorithm can obtain the shortest path planning in a shorter time with better performance. Finally, the grasping experiment is carried out in a real robotic arm transmission line inspection scene. The robotic arm path planning algorithm and visual servo control proposed in this paper are combined to carry out grasping experiments in a static cluttered environment. The experimental results show that the algorithm proposed in this paper has the best grasping success rate, servo success rate, and grasping similarity. In addition, the average errors in x-axis, y-axis and z-axis of the proposed method are 0.19cm, 1.11cm and 0.18cm, respectively, which show a good grasping accuracy, indicating that the control method proposed in this paper can be applied to the real transmission line inspection work scenarios.

An Image-Based Visual Servoing Control (IBVS) structure for target tracking by Unmanned Aerial Vehicles (UAVs) is presented. The scheme contains two stages. The first one is a sliding-model controller (SMC) that allows one to track a target with a UAV; the control strategy is designed in the function of the image. The proposed SMC control strategy is commonly used in control systems that present high non-linearities and that are always exposed to external disturbances; these disturbances can be caused by environmental conditions or induced by the estimation of the position and/or velocity of the target to be tracked. In the second instance, a controller is placed to compensate the UAV dynamics; this is a controller that allows one to compensate the velocity errors that are produced by the dynamic effects of the UAV. In addition, the corresponding stability analysis of the sliding mode-based visual servo controller and the sliding mode dynamic compensation control is presented. The proposed control scheme employs the kinematics and dynamics of the robot by presenting a cascade control based on the same control strategy. In order to evaluate the proposed scheme for tracking moving targets, experimental tests are carried out in a semi-structured working environment with the hexarotor-type aerial robot. For detection and image processing, the Opencv C++ library is used; the data are published in an ROS topic at a frequency of 50 Hz. The robot controller is implemented in the mathematical software Matlab.

This paper aims to develop a visual servo control of a robotic manipulator for cherry tomato harvesting. In the robotic manipulator, an RGB-depth camera was mounted to the end effector to acquire the poses of the target cherry tomatoes in space. The eye-in-hand-based visual servo controller guides the end effector to implement eye–hand coordination to harvest the target cherry tomatoes, in which a hybrid visual servo control method (HVSC) with the fuzzy dynamic control parameters was proposed by combining position-based visual servo (PBVS) control and image-based visual servo (IBVS) control for the tradeoff of both performances. In addition, a novel cutting and clipping integrated mechanism was designed to pick the target cherry tomatoes. The proposed tomato-harvesting robotic manipulator with HVSC was validated and evaluated in a laboratory testbed based on harvesting implementation. The results show that the developed robotic manipulator using HVSC has an average harvesting time of 9.40 s/per and an average harvesting success rate of 96.25% in picking cherry tomatoes.

Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) struggle to balance end effector pose accuracy and robustness in apple picking. They are also prone to target loss and control singularities. A progressive Hybrid Automatic Switching Visual Servoing (HAVS) method is proposed and applied to an apple-picking robotic system. HAVS integrates PBVS and IBVS to coordinate control of the manipulator end effector pose. A depth-based switching function is designed. When target depth is below an optimal threshold, the controller switches to PBVS for precise final positioning. This reduces target loss and control singularities. An adaptive proportional-derivative (PD) controller with fuzzy gain scheduling updates the control gains online to enhance responsiveness and stability. The hardware consists of a six-axis manipulator, a depth camera, and a mobile base. You Only Look Once version 5 (YOLOv5) performs apple detection and generates control commands. Indoors, success rate was 96%, which was 4 and 10 percentage points higher than PBVS only and IBVS only. Average picking time was 12.5 s, 0.3 s, and 1.1 s shorter. Outdoors, success rate was 87.5%, average time was 13.2 s, and damage rate was 4.2%. This method provides a reference implementation for visual servo control in agricultural picking robots.