Visual Servo Control Based On Multi-View Geometry

Robotic systems play an important role in many areas.For different application scenarios,it is necessary to combine the robot with different sensors to improve the intelligence of the whole system.Since vision sensors can provide rich perception information,they have been widely used in various robot platforms.With the rapid development of science and technology,many researchers focus on utilizing the visuall information to guide the robot to accomplish diverse tasks,such as localization,navigation and control.Among these tasks,visual servo control is one of the most discussed issues.Visual servo control is aimed at driving the robot to achieve specific motion objectives via real-time image feedback.To address the visual servo control,computer vision techniques are exploited firstly to extract feature information from two-dimensional images to construct feedback errors.Then,by using the control theory and the constructed error signals,controllers are designed to accomplish the specified motion task.Since the visual servo system generally subjected to various model uncertainties and constraints,it is challenging to develop robust and efficient servo strategies for different scenarios.Based on the state-of-art study,this dissertation researches on visual servo control with multi-view geometry.The main work and contributions are summarized as follows:· A brief review of the background,overview,and related works on visual servo control is provided.· Research on homography-based moving object asymptotic tracking.A homography-based visual servo controller is developed for a robot arm to track a moving object in three di-mensional space with a fixed relative pose.A monocular camera is mounted to the robot arm to provide visual information,and then homography is exploited to obtain the orienta-tion and scaled translation of the camera for controller design.Considering the unknown moving object’s velocities and distance information,a robust nonlinear visual controller is developed.Theoretical analysis shows that the proposed controller can achieve asymptotic tracking.Simulation results validate the effectiveness of the proposed approach.· Research on trifocal tensor-based 6 degrees-of-freedom visual regulation.The trifocal tensor model among the current,desired,and initial views is introduced to describe the geometric relationship.Then,the tensor elements are refined to construct the visual feedback without resorting to explicit estimation of the camera pose.Based on the extracted tensor features,an adaptive controller is designed to drive the camera to a desired pose and compensate for the unknown distance scale factor.Moreover,Lyapunov-based techniques are exploited to analyze the system stability and convergence domain.Simulation and experimental results are provided to validate the theoretical analysis.· Research on unified visual control of mobile robots with an uncalibrated camera.A vision-based strategy is proposed for the unified tracking and regulation control of a wheeled mobile robot equipped with an uncalibrated monocular camera.An online implementable method based on the proj ection homography technique is first proposed to estimate the partial camera intrinsic parameters.By exploiting the obtained camera intrinsic parameters,the orientation and scaled position information of the mobile robot is extracted from the images to construct the error system.Then,an adaptive continuous controller is designed to address both the trajectory tracking and regulation problems with input saturation.The system uncertainties regarding the distance information and the camera intrinsic parameters are fully taken into consideration as well as the nonholonomic constraint.Moreover,stability analysis shows that the proposed controller can achieve asymptotic tracking and regulation in the presenceof system uncertainties.Simulation and experimental results validate the effectiveness of the proposed strategy.· Research on vision-based trajectory tracking and depth estimation of mobile robots.A visual servo approach is developed for the trajectory tracking control and depth estimation problem of a mobile robot without a priori knowledge about desired velocities.By exploiting the mul-tiple images captured by the on-board camera,the current and desired poses of the mobile robot are reconstructed to define system errors.Then,an adaptive time-varying controller is proposed to achieve the trajectory tracking task in the presence of nonholonomic constraint and unknown depth parameters.Most of previous works require the measurement of the de-sired velocity information to facilitate the controller design,leading to tedious offline compu-tation.To eliminate this requirement,the desired velocities are estimated in real-time based on a reduced order observer.Moreover,an augmented update law is designed to compensate for the unknown depth parameters and identify the inverse depth constant.The Lyapunov-based method is employed to prove that the proposed controller achieves asymptotic tracking,and the inverse depth estimate converges to its actual va lue provided that a persistent exci-tation condition is satisfied.Subsequently,a robust data driven algorithm is introduced to ensure the convergence of the inverse depth estimate under a relaxed finite excitation con-dition.Simulation and experimental results are provided to demonstrate the effectiveness of the proposed approach.The conclusions are drawn with future work at the end of the dissertation.