Robot Visual Servoing Using Discontinuous Control

This paper presents a review of characteristics of the two main visual servoing methods: image-based visual servoing and position-based visual servoing. First, a generic structure is defined with an emphasis on the same system inputs and outputs for the two methods. The stability, robustness, sensitivity, and other general characteristics of the methods are presented. To overcome the dependency of both methods on a known target model, a midpoint triangulation method is proposed to estimate the unknown target model for robot visual servoing.

Ball balancers are nonlinear, electromechanical, multivariable, open-loop unstable systems widely used in research laboratories, aerospace, military, and automotive industries to evaluate control mechanism effectiveness. The inherent difficulty in precisely managing ball position, combined with actuator saturation and system sensitivity to disturbances, makes trajectory tracking a persistent challenge. Conventional controllers often exhibit oscillatory responses with steady-state errors exceeding acceptable limits. Sliding mode control (SMC) offers robustness against model uncertainties; however, chattering finite-frequency, finite-amplitude oscillations near the sliding surface caused by switching imperfections, time delays, and actuator dynamics remain a significant limitation. This study addresses chattering through explicit vibration model compensation integrated into the SMC design for a 2-DOF ball balancer system using a visual servoing approach. A double-loop control architecture is implemented, where the inner loop handles servo angular position control and the outer loop manages ball position tracking through visual servoing feedback. The sliding mode controller is designed with a power rate reaching law, synthesizing two control laws: one with explicit vibration model compensation incorporating damping and stiffness terms, and one without. Experimental validation confirmed that SMC with compensation achieved significantly reduced steady-state error (0.034 mm vs. 0.386 mm) and lower overshoot (3.95% vs. 13.81%) compared to the uncompensated variant, with chattering amplitude reduced by approximately 72%.

Visual servoing (VS) is an automatic control technique which uses the feedback of visual information to guide the robot motion to converge to the desired location. This paper proposes new moment-based features for robust VS with spheres, especially in cameras obeying the unified model. Specifically, we introduce a virtual plane to reload contours of image projections of spheres, in the process of which image noises will however be greatly magnified and induce large converge error. To solve this, new moment-based features are proposed and extracted on this virtual plane to enhance the robustness of VS to calibration errors and image noises. Simulation results verify the effectiveness of the proposed scheme and demonstrate the high robustness of these set of new features to image noises. The altering trends of converge errors versus increasing noise levels are randomly tested to present the obvious advantage of new moment-based features over the usual ones. At last, a path-planning based VS application is performed with fisheye views of some spheres, benefiting from the robustness and an interpolation-based tracking scheme.

Visual servoing provides the flexibility of robot control in dynamic environments, but three-dimensional (3-D) robot visual servoing is challenging due to the 2-D nature of the image space. Adaptive homography-based visual servoing (HBVS) is effective for 3-D robot pose control under various parametric uncertainties, but the robot dynamics is seldom considered under uncalibrated camera parameters. This paper proposes a dynamics-based adaptive HBVS method for 3-D pose tracking of robot manipulators using an eye-to-hand monocular camera with unknown extrinsic parameters, where a composite learning law is designed to achieve parameter convergence without the stringent persistent excitation condition. Compared with existing adaptive HBVS methods, the proposed method accounts for robot dynamics in the control design to enhance control performance, calibrates extrinsic parameters online under a weak condition of interval excitation, and does not require measuring the rotation of a reference plane or finding valid vanishing points. Experiments on an articulated robot with 7 degrees of freedom, named Franka Emika Panda, have verified that the proposed method performs well for both camera extrinsic online calibration and robot 3-D pose tracking.

This article presents an original motion control strategy for robot manipulators based on the coupling of the inverse dynamics method with the so-called second-order sliding mode control approach. Using this method, in principle, all the coupling non-linearities in the dynamical model of the manipulator are compensated, transforming the multi-input non-linear system into a linear and decoupled one. Actually, since the inverse dynamics relies on an identified model, some residual uncertain terms remain and perturb the linear and decoupled system. This motivates the use of a robust control design approach to complete the control scheme. In this article the sliding mode control methodology is adopted. Sliding mode control has many appreciable features, such as design simplicity and robustness versus a wide class of uncertainties and disturbances. Yet conventional sliding mode control seems inappropriate to be applied in robotics since it can generate the so-called chattering effect, which can be destructive for the controlled robot. In this article, this problem is suitably circumvented by designing a second-order sliding mode controller capable of generating a continuous control law making the proposed sliding mode controller actually applicable to industrial robots. To build the inverse dynamics part of the proposed controller, a suitable dynamical model of the system has been formulated, and its parameters have been accurately identified relying on a practical MIMO identification procedure recently devised. The proposed inverse dynamics-based second-order sliding mode controller has been experimentally tested on a COMAU SMART3-S2 industrial manipulator, demonstrating the tracking properties and the good performances of the controlled system.

Sliding mode control (SMC) is a powerful and robust control method. SMC methods have been widely studied in the last three decades from theoretical concepts to industrial applications [1]-[3]. Higher-order sliding mode controllers have recently been addressed to improve the system responses [1]. However, when designing a control for a plant it is sometimes more beneficial to use combined techniques, using SMC in conjunction with other methods such as backstepping, passivity, flatness and even other traditional control design methods including H ∞ , proportional-integral-derivative (PID) and self-tuning. Note that PID control design techniques may also be used for designing the sliding surface. A drawback of the SMC methods may be unwanted chattering resulting from discontinuous control. There are many methods which can be employed to reduce chattering, for example, using a continuous approximation of the discontinuous control, and a combination of continuous and discontinuous sliding mode controllers. Chattering may also be reduced using the higher-order SMC [4] and dynamic sliding mode control [4, 5].

In this paper, a tuning methodology based on predictive model approach for visual servoing system is investigated. The proposed approach uses features prediction based method to estimate the camera velocity and thus to calculate the optimal control tuning parameter. In order to have a faster convergence and in the same time a desired behaviour of the servoing system, a new control parameter is computed every sampling time. To evaluate the designed tuning strategy, a visual servoing architecture with an eye-in-hand configuration has been considered. The experimental results showed that the proposed tuning algorithm based on prediction model has a stable and convergent behavior when dealing with visual servoing applications. To the knowledge of the authors, the proposed methodology is the first approach which enables an automatic selection of control parameter for the proportional visual control law.

In this paper, sliding mode control (SMC) technique is used to control the speed of DC motor. The performance of the SMC is judged via MATLAB simulations using linear model of the DC motor and known disturbance. SMC is then compared with PI controller. The simulation result shows that the sliding mode controller (SMCr) is superior controller than PI for the speed control of DC motor. Since the SMC is robust in presence of disturbances, the desired speed is perfectly tracked. The problem of chattering, resulting from discontinuous controller, is handled by pseudo sliding with smooth control action.

In this paper, robust nonlinear control of moment-based visual servoing has been proposed. Due to the nonlinear nature of system, proportional integral sliding mode controller could improve the performance and robustness of visual servoing. The proposed method not only increases domain of attraction even in extraneous image regions, but also it achieves convergence to the target point despite of uncertainties. The stability of the controller is analyzed by Lyapunov's theorem. Furthermore, Integrated control of 4 DOF camera motions in this work, drastically improves the accuracy in compare with the kernel-based sliding mode visual servoing [1]. Different experiments are done to demonstrate considerable performance improvements on 5 DOF industrial robot.

ROS-Industrial based robotic cell for Industry 4.0: Eye-in-hand stereo camera and visual servoing for flexible, fast, and accurate picking and hooking in the production line

Visual Servoing (VS) has so many advantages and potentialities in applications that it has been studied intensively from several viewpoints. To realize VS, we have to deal with constraints necessary for VS such as keeping all features of moving target in the field of view and avoiding saturation of manipulator torque limitation. In this paper, we propose a new method to construct VS system taking the constraints for VS into account explicitly based on reachable sets for systems with bounded disturbance. We propose also a switching strategy to get the controller more high performance in addition to constraint fulfillment. We demonstrate efficacies of this method by performing a experiment of VS using 6link manipulator PA10.

Clothing structure design is a key link in the clothing manufacturing process, and the traditional design methods rely on the experience and skills of designers. With the development and application of digital technology in the apparel industry, it has greatly promoted the innovation and development of apparel manufacturing. In this paper, we combine the depth estimation and vision controller methods to construct an image Jacobi matrix to realize the control of the hand-eye mapping relationship of the robot's visual servo, which represents the mapping relationship between the robot's joint speed to the end speed. Using Kalman filtering algorithm, the image Jacobi matrix to be estimated is used as the system state to estimate the system state, so as to achieve the control of the stitch and displacement of the garment sewing, and, at the same time, capture the visual information contained in the garment to optimize the design of the garment structure. For the optimized designed garments using the Jacobi matrix, 4# has the highest mean comfort rating of 4.5 and above. The mean value of satisfaction evaluation for ease of movement and overall comfort of the optimized garment went up to 4. It is evident that the overall comfort of the garment optimized by the image Jacobi matrix algorithm has been significantly improved.

This paper focuses on maintaining the liquid level in tank two in the coupled tank control system with the help of the sliding mode control (SMC). SMC is the potent controller for coupled tank liquid level system under tracking conditions. Sliding mode controller (SMC) is nonlinear control it applies a discontinuous control signal. The main advantages of sliding mode are low sensitivity to plant parameter variations and disturbances. To maintain desired liquid levels in a nonlinear coupled tank is a great challenging task so this problem better handled by SMC. To control this liquid level process a SMC strategy is used we have first order SMC and second order SMC. Regulating this system in less time with proper control signal is not possible in first order SMC due to chattering is obtained which yields unwanted control signal. To fill tank-2 in less time with smooth control signal is possible in higher order SMC. Next, second order SMC is applied and stability is reached fast and no chattering phenomenon. These can be shown in the simulation results with the help of MATLAB. This application report describes how to develop SMC for liquid level control in coupled tank system.

An optimal adaptive robust PID controller subject to fuzzy rules and sliding modes for MIMO uncertain chaotic systems

This paper considers image-guided assembly for large composite panels. By using fiducial markers on the panels and robot gripper mounted cameras, we are able to use an industrial robot to align the panels to sub-millimeter accuracy. We considered two commonly used visual servoing schemes: position-based visual servoing (PBVS) and image-based visual servoing (IBVS). It has been noted that IBVS possesses superior robustness with respect to the camera calibration accuracy. However, we have found that in our case, PBVS is both faster and slightly more accurate than IBVS. This result is due to the fact that the visual servoing target in the image plane is derived from a reference target, which depends on the accuracy of the camera model. This additional dependency essentially nullifies the robustness advantage of IBVS. We also implemented a simple scheme to combine inputs from multiple cameras to improve the visual servoing accuracy. Both simulation and experimental results are included to show the effectiveness of visual servoing in an industrial setting.