Visual Servo Tracking Controller Research Articles

A Hybrid Model and Data-Driven Vision-Based Framework for the Detection, Tracking and Surveillance of Dynamic Coastlines Using a Multirotor UAV

A hybrid model-based and data-driven framework is proposed in this paper for autonomous coastline surveillance using an unmanned aerial vehicle. The proposed approach comprises three individual neural network-assisted modules that work together to estimate the state of the target (i.e., shoreline) to contribute to its identification and tracking. The shoreline is first detected through image segmentation using a Convolutional Neural Network. The part of the segmented image that includes the detected shoreline is then fed into a CNN real-time optical flow estimator. The position of pixels belonging to the detected shoreline, as well as the initial approximation of the shoreline motion, are incorporated into a neural network-aided Extended Kalman Filter that learns from data and can provide on-line motion estimation of the shoreline (i.e., position and velocity in the presence of waves) using the system and measurement models with partial knowledge. Finally, the estimated feedback is provided to a Partitioned Visual Servo tracking controller for autonomous multirotor navigation along the coast, ensuring that the latter will always remain inside the onboard camera field of view. A series of outdoor comparative studies using an octocopter flying along the shoreline in various weather and beach settings demonstrate the effectiveness of the suggested architecture.

Image-Based Visual Servo Tracking Control of a Ground Moving Target for a Fixed-Wing Unmanned Aerial Vehicle

Image-Based Visual Servo Tracking Control of a Ground Moving Target for a Fixed-Wing Unmanned Aerial Vehicle

Visual Servo Tracking of Wheeled Mobile Robots With Unknown Extrinsic Parameters

In most visual servoing methods for wheeled mobile robots, the camera is supposed at the center of the robot, while it will bring much convenience for practical use when putting the camera a little away from the robot center. In this paper, to handle the challenge that is incurred by the uncalibrated translational camera-to-robot parameters, a novel visual servoing scheme is designed for wheeled mobile robots to track a given trajectory, which is defined by a sequence of images recorded previously. The trajectory tracking errors are defined with respect to both current and desired poses of the robot, and the open-loop error system is obtained after analyzing kinematic models of the visual robot system. Subsequently, to compensate both unknown extrinsic parameters and the visual depth, an adaptive visual servo tracking controller is elaborately designed, with disposing the nonholonomic motion constraint of the mobile robot. The tracking errors are strictly proven converging to zero asymptotically by employing Lyapunov techniques and Barbalat's lemma. Both simulation and comparative experiments are conducted to prove that the proposed strategy can drive the robot track the desired trajectory efficiently, even translational camera-to-robot parameters are unknown.

Visual Servo Tracking Control for Slider Robot Using CMAC Neural Network Recognition Approach

This paper describes an image-based visual servo tracking control scheme using CMAC neural network as object recognition feedback methodology. A web camera based image capture system is mounted on the slider robot to capture the desired object and a CMAC (Cerebellar Model Articulation Controller) based object recognition scheme is developed to recognize the captured object image. Comparing the relative location of the recognized object and the web camera, then the position feedback signal can be obtained. Using the feedback signal, a PID (Proportional-Integral-Derivative) controller is designed to track the desired object by moving a single-axis slider robot, such that the captured object image located on the center of the captured frame always.

MONOCULAR CAMERA VISUAL SERVO CONTROL OF WHEELED MOBILE ROBOTS

In this paper, a visual servo tracking controller for a wheeledmobile robot (WMR) is developed that utilizes feedback from amonocular camera system that is mounted with a fixed position andorientation (i.

Adaptive Homography-Based Visual Servo Tracking Control via a Quaternion Formulation

In this paper, an adaptive homography-based visual servo tracking controller is developed for the camera-in-hand problem using a quaternion formulation to represent rotation tracking error. The desired trajectory in the tracking problem is encoded by a sequence of images (e.g., a video sequence), and Lyapunov methods are employed to facilitate the control design and the stability analysis. An adaptive estimation law is designed to compensate for the lack of unknown depth information. Experimental results are provided to demonstrate the performance of the visual servo tracking controller.

Navigation function-based visual servo control

In this paper, the mapping between the desired camera feature vector and the desired camera pose (i.e., the position and orientation) is investigated to develop a measurable image Jacobian-like matrix. An image-space path planner is then proposed to generate a desired image trajectory based on this measurable image Jacobian-like matrix and an image-space navigation function (NF) (i.e., a special potential field function) while satisfying rigid body constraints. An adaptive, homography-based visual servo tracking controller is then developed to navigate the position and orientation of a camera held by the end-effector of a robot manipulator to a goal position and orientation along the desired image-space trajectory while ensuring the target points remain visible (i.e., the target points avoid self-occlusion and remain in the field-of-view (FOV)) under certain technical restrictions. Due to the inherent nonlinear nature of the problem and the lack of depth information from a monocular system, a Lyapunov-based analysis is used to analyze the path planner and the adaptive controller. Simulation results are provided to illustrate the performance of the proposed approach.

Homography-based visual servo tracking control of a wheeled mobile robot

A visual servo tracking controller is developed in this paper for a monocular camera system mounted on an underactuated wheeled mobile robot (WMR) subject to nonholonomic motion constraints (i.e., the camera-in-hand problem). A prerecorded image sequence (e.g., a video) of three target points is used to define a desired trajectory for the WMR. By comparing the target points from a stationary reference image with the corresponding target points in the live image and the prerecorded sequence of images, projective geometric relationships are exploited to construct Euclidean homographies. The information obtained by decomposing the Euclidean homography is used to develop a kinematic controller. A Lyapunov-based analysis is used to develop an adaptive update law to actively compensate for the lack of depth information required for the translation error system. Experimental results are provided to demonstrate the control design.