Visual Trajectory Tracking Research Articles

Stability Analysis of Neutral Fractional Stochastic Differential Equations Driven by Mixed Brownian Motion and Subfractional Brownian Motion With Applications in the I-Cub Robot

Exponential stability is often used to barricade stochastic disturbance in visual trajectory tracking of a robotic system. In this work, a nonlinear mathematical model to deal with the exponential stability of visual trajectory in the I-cub robot is developed. The main contributions of this article are: 1) exponential stability is studied for the first time in the literature for the neutral fractional stochastic differential equations (NFSDEs) driven by mixed Brownian motion (Bm) and subfractional Bm (sub-fBm); 2) existence and stability results are derived based on the Banach contraction principle, semi-group theory, and fractional calculus in stochastic settings; 3) stability results of mixed Bm and sub-fBm are established and applied to avoid dense environment stochastic disturbance in the visual trajectory of the I-cub robot; and 4) stability of sub-fBm is entrusted in the dense environment even in small particles from the shorter length. A weak solution of the proposed result ensures a sufficiently smooth solution of the considered robotic system. The obtained results are new and innovative in the sense that the proposed algorithms have several advantages for gaze shift framed to the I-cub robot, including helping to maintain the fixation of eye movement in the microscopic laboratory. Furthermore, the results are used to enhance the performance of decreasing rendering gaze shift to achieve high-quality visual tracking.

Visual Trajectory Tracking of Wheeled Mobile Robots With Uncalibrated Camera Extrinsic Parameters

In this article, the eye-in-hand visual trajectory tracking control problem of wheeled mobile robots (WMRs) is considered. Different from the conventional vision-based approaches, the monocular camera is not required to be mounted at the center of WMR, and thus the derived visual model is subject to not only the nonholonomic constraint but also the unknown camera extrinsic parameters. To ensure the WMR can track a desired trajectory effectively, a combined observation/control strategy is proposed. First, a concurrent learning observer is designed to identify the camera extrinsic parameters with measurable visual signals. Then, with the aid of the estimated parameters, a nonlinear controller is presented to achieve the tracking task. The closed-loop system stability is analyzed with Lyapunov methods, showing that both the estimation and tracking errors are asymptotically convergent to zero. Simulation and experimental results are provided to validate the developed approach.

High order differential feedback controller design and implementation for a Stewart platform

Stewart platform or other parallel manipulators with a Stewart structure are commonly used in flight simulators, surgical operations, medical rehabilitation processes, machine tools, industrial applications, etc. Therefore, researchers have paid attention to position control of these manipulators in addition to their design and development process. In this study, a developed Stewart platform and its inverse kinematic analysis are presented first. Then, a model-free control scheme called a high order differential feedback controller scheme is designed for the Stewart platform in order to improve its trajectory tracking performance and robustness against to different reference trajectories. Real-time trajectory tracking experiments with varied reference trajectories are carried out to show the robustness and effectiveness of the high order differential feedback controller scheme compared to the traditional proportional–integral–derivative controller of which the parameters are optimally tuned. The obtained visual trajectory tracking results and numerical performance results based on error-based performance measurement metrics such as integral of absolute error, integral of squared error, and integral of time-weighted absolute error are provided for both the proposed high order differential feedback controller scheme and the optimal tuned proportional–integral–derivative controller. Experimental results show that the proposed high order differential feedback controller scheme is more robust than the proportional–integral–derivative controller. Furthermore, the high order differential feedback controller scheme has superiority in both transient and steady-state responses and even the parameters of the proportional–integral–derivative controller are optimally tuned.

Aerial Grasping with a Lightweight Manipulator Based on Multi-Objective Optimization and Visual Compensation.

Autonomous grasping with an aerial manipulator in the applications of aerial transportation and manipulation is still a challenging problem because of the complex kinematics/dynamics and motion constraints of the coupled rotors-manipulator system. The paper develops a novel aerial manipulation system with a lightweight manipulator, an X8 coaxial octocopter and onboard visual tracking system. To implement autonomous grasping control, we develop a novel and efficient approach that includes trajectory planning, visual trajectory tracking and kinematic compensation. Trajectory planning for aerial grasping control is formulated as a multi-objective optimization problem, while motion constraints and collision avoidance are considered in the optimization. A genetic method is applied to obtain the optimal solution. A kinematic compensation-based visual trajectory tracking is introduced to address the coupled affection between the manipulator and octocopter, with the advantage of discarding the complex dynamic parameter calibration. Finally, several experiments are performed to verify the effectiveness of the proposed approach.

Hybrid Visual Servo Trajectory Tracking of Wheeled Mobile Robots

A hybrid visual trajectory strategy is developed for wheeled mobile robots equipped with onboard vision systems, wherein the 2.5-D visual servoing framework is utilized to enhance trajectory tracking behavior and help to retain visual objects in horizon of the camera. First, according to the current image, the reference image, and the desired images sequence, compound system errors are constructed by both image features and robot orientation. Subsequently, the open-loop error dynamics can be acquired after introducing an error transformation. On this basis, an adaptive controller is developed to achieve the visual servo tracking objective, where the feature depth is compensated online by a parameter updating mechanism. As demonstrated by Lyapunov techniques and Barbalat’s Lemma, the proposed visual trajectory tracking controller makes the system errors converge to zero asymptotically in spite of the unknown scene depth. Comparative simulation results are provided to validate the performance of the presented strategy.

Trifocal Tensor-Based Adaptive Visual Trajectory Tracking Control of Mobile Robots.

In this paper, a trifocal tensor-based approach is proposed for the visual trajectory tracking task of a nonholonomic mobile robot equipped with a roughly installed monocular camera. The desired trajectory is expressed by a set of prerecorded images, and the robot is regulated to track the desired trajectory using visual feedback. Trifocal tensor is exploited to obtain the orientation and scaled position information used in the control system, and it works for general scenes owing to the generality of trifocal tensor. In the previous works, the start, current, and final images are required to share enough visual information to estimate the trifocal tensor. However, this requirement can be easily violated for perspective cameras with limited field of view. In this paper, key frame strategy is proposed to loosen this requirement, extending the workspace of the visual servo system. Considering the unknown depth and extrinsic parameters (installing position of the camera), an adaptive controller is developed based on Lyapunov methods. The proposed control strategy works for almost all practical circumstances, including both trajectory tracking and pose regulation tasks. Simulations are made based on the virtual experimentation platform (V-REP) to evaluate the effectiveness of the proposed approach.

Visual trajectory tracking of industrial manipulator with iterative learning control

Purpose – The purpose of this paper is to propose a more efficient strategy, which is easier to implement, i.e. the engineer can directly operate the target object without the robot to do a demonstration, and the manipulator is regulated to track the trajectory using vision feedback repetitively. Generally, the applications of industrial robotic manipulators are based on teaching playback strategy, i.e. the engineer should directly operate the manipulator to perform a demonstration and then the manipulator uses the recorded driving signals to perform repetitive tasks. Design/methodology/approach – In the teaching process, the engineer grasps the object with a camera on it to do a demonstration, during which a series of images are recorded. The desired trajectory is defined by the homography between the images captured at current and final poses. Tracking error is directly defined by the homography matrix, without 3D reconstruction. Model-free feedback-assisted iterative learning control strategy is used for repetitive tracking, where feed-forward control signal is generated by iterative learning control strategy and feedback control signal is generated by direct feedback control. Findings – The proposed framework is able to perform precise trajectory tracking by iterative learning, and is model-free so that the singularity problem is avoided which often occurs in conventional Jacobean-based visual servo systems. Besides, the framework is robust to image noise, which is shown in simulations and experiments. Originality/value – The proposed framework is model-free, so that it is more flexible for industrial use and easier to implement. Satisfactory tracking performance can be achieved in the presence of image noise. System convergence is analyzed and experiments are provided for evaluation.

Vision servoing of robot systems using piecewise continuous controllers and observers

This paper deals with the visual servoing of X-Y robot systems using low cost CCD camera. The proposed approach is based on the theory of piecewise continuous systems which are a particular class of hybrid systems with autonomous switching and controlled impulses. Visual trajectory tracking systems comprising piecewise continuous controllers and observers, are developed. Real-time results are given to illustrate the effectiveness of the proposed visual control system.

予見制御による視覚軌道追従制御法

予見制御による視覚軌道追従制御法