A review on visual servoing for underwater vehicle manipulation systems automatic control and case study

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.

U ovom preglednom radu su prikazane metode vizuelnog upravljanja robotskih sistema, sa primarnim fokusom na mobilne robote sa diferencijalnim pogonom. Analizirane su standardne metode vizuelnog upravljanja bazirane na (i) greškama u parametrima slike (engl. Image-Based Visual Servoing - IBVS) i (ii) izdvojenim karakteristikama sa slike neophodnim za estimaciju položaja izabranog objekta (engl. Position-Based Visual Servoing - PBVS) i poređene sa novom metodom direktnog vizuelnog upravljanja (engl. Direct Visual Servoing - DVS). U poređenju sa IBVS i PBVS metodama, DVS metod se odlikuje višom tačnošću, ali i manjim domenom konvergencije. Zbog ovog razloga je DVS metod upravljanja pogodan za integraciju u hibridne sisteme vizuelnog upravljanja. Takođe, predstavljeni su radovi koji unapređuju sistem vizuelnog upravljanja korišćenjem stereo sistema (sistem sa dve kamere). Stereo sistem, u poređenju sa alternativnim metodama, omogućava tačniju ocenu dubine karakterističnih objekata sa slike, koja je neophodna za zadatke vizuelnog upravljanja. Predmet analize su i radovi koji integrišu tehnike veštačke inteligencije u sistem vizuelnog upravljanja. Ovim tehnikama sistemi vizuelnog upravljanja dobijaju mogućnost da uče, čime se njihov domen primene znatno proširuje. Na kraju, napominje se i mogućnost integracije vizuelne odometrije u sisteme vizuelnog upravljanja, što prouzrokuje povećanje robusnosti čitavog robotskog sistema.

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.

This study proposes an optimized hybrid visual servoing approach to overcome the imperfections of classical two-dimensional, three-dimensional and hybrid visual servoing methods. These imperfections are mostly convergence issues, non-optimized trajectories, expensive calculations and singularities. The proposed method provides more efficient optimized trajectories with shorter camera path for the robot than image-based and classical hybrid visual servoing methods. Moreover, it is less likely to lose the object from the camera field of view, and it is more robust to camera calibration than the classical position-based and hybrid visual servoing methods. The drawbacks in two-dimensional visual servoing are mostly related to the camera retreat and rotational motions. To tackle these drawbacks, rotations and translations in Z-axis have been separately controlled from three-dimensional estimation of the visual features. The pseudo-inverse of the proposed interaction matrix is approximated by a neuro-fuzzy neural network called local linear model tree. Using local linear model tree, the controller avoids the singularities and ill-conditioning of the proposed interaction matrix and makes it robust to image noises and camera parameters. The proposed method has been compared with classical image-based, position-based and hybrid visual servoing methods, both in simulation and in the real world using a 7-degree-of-freedom arm robot.

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.

This paper presents a method to combine voice control and hybrid visual servoing for a continuum robot. In this design, the continuum robot autonomously tracks the element of interest and maintains its position in the safe region based on image information. A method of hybrid visual servoing is proposed to integrate 2D image-based visual servoing (IBVS) to ensure the elements of interests under visibility constraints and 3D position-based visual servoing (PBVS) to adjust the robot to a desired pose in the constraint region. The robotic camera thus provides stable image while tracking the element of interest. In this design, the user can move the continuum robot to acquire the desired view by voice commands. In voice control, the system can recognize connecting words to control the robot. All software modules are integrated using Robot Operating System (ROS) for controlling the continuum robot. Experimental results obtained from a prototype continuum robot verify the effectiveness of the proposed method.

This thesis deals with Visual Servoing and its strictly connected disciplines like projective geometry, image processing, robotics and non-linear control. More specifically the work addresses the problem to control a robotic manipulator through one of the largely used Visual Servoing techniques: the Image Based Visual Servoing (IBVS). In Image Based Visual Servoing the robot is driven by on-line performing a feedback control loop that is closed directly in the 2D space of the camera sensor. The work considers the case of a monocular system with the only camera mounted on the robot end effector (eye in hand configuration). Through IBVS the system can be positioned with respect to a 3D fixed target by minimizing the differences between its initial view and its goal view, corresponding respectively to the initial and the goal system configurations: the robot Cartesian Motion is thus generated only by means of visual informations. However, the execution of a positioning control task by IBVS is not straightforward because singularity problems may occur and local minima may be reached where the reached image is very close to the target one but the 3D positioning task is far from being fulfilled: this happens in particular for large camera displacements, when the the initial and the goal target views are noticeably different. To overcame singularity and local minima drawbacks, maintaining the good properties of IBVS robustness with respect to modeling and camera calibration errors, an opportune image path planning can be exploited. This work deals with the problem of generating opportune image plane trajectories for tracked points of the servoing control scheme (a trajectory is made of a path plus a time law). The generated image plane paths must be feasible i.e. they must be compliant with rigid body motion of the camera with respect to the object so as to avoid image jacobian singularities and local minima problems. In addition, the image planned trajectories must generate camera velocity screws which are smooth and within the allowed bounds of the robot. We will show that a scaled 3D motion planning algorithm can be devised in order to generate feasible image plane trajectories. Since the paths in the image are off-line generated it is also possible to tune the planning parameters so as to maintain the target inside the camera field of view even if, in some unfortunate cases, the feature target points would leave the camera images due to 3D robot motions. To test the validity of the proposed approach some both experiments and simulations results have been reported taking also into account the influence of noise in the path planning strategy. The experiments have been realized with a 6DOF anthropomorphic manipulator with a fire-wire camera installed on its end effector: the results demonstrate the good performances and the feasibility of the proposed approach.

In this paper, an image-based uncalibrated visual servo scheme is proposed for image tracking tasks in highly disturbed environment, such as a remotely operated vehicle performing observation or investigation influenced by severe underwater current. Under the conditions of unknown target model and camera parameters, the control framework applies the scale invariant feature transform (SIFT) technique to extract image features. Furthermore, a robust adaptive control law is implemented to overcome the effect caused by camera parameters. Then by using three different types of camera’s motion: pan, tilt, and zoom to achieve the visual servo mission by maintaining the target always at the central position on the image plane.

This paper addresses the challenges of choosing proper image features for planar symmetric shape objects and designing visual servoing controller to enhance the tracking performance in image-based visual servoing (IBVS). Six image moments are chosen as the image features and the analytical image interaction matrix related to the image features are derived. A controller is designed to efficiently increase the robustness of the visual servoing system. Experimental results on a 6-DOF robot visual servoing system are provided to illustrate the effectiveness of the proposed method.

This paper presents a self-memory prediction model to mitigate the effects of image based visual servoing (IBVS) system under uncertainty. The performance of IBVS system is easily influenced by different tasks, diverse environments and uncertain disturbances. Through building a self-memory prediction model to keep previous movement tendency in the every current movement, the framework of a self-memory is proposed for incremental self-learning capability. The main challenge in IBVS system is avoid abrupt change in the operation of the system. The proposed approach is processed from state pattern, and introduces the memory function which contain multiple historical observed values. According to past events, predict the current state and avoid abrupt changes in the every current movement. Simulations on IBVS system illustrate the effectiveness of the proposed method.

Prosthesis is a device that replaces a missing body part. These devices are used to reinstate the lost functionality due to an amputation. Above elbow amputees are equipped with trans-humeral prostheses. The existing prostheses use biological signals such as electromyography (EMG) in combine with external sensors for the controlling purposes. Vision is the latest addition to external sensor domain to eliminate the drawbacks of existing hybrid prostheses. This paper proposes a vision based task planning method in combination with available EMG signals to plan reach-to-grasp tasks of a trans-humeral prosthesis. The visual servoing system consists of an Image Based Visual Servoing (IBVS) system in conjunction with an Artificial Neural Network (ANN) to center the object in the image frame. Moreover, a gradient descent algorithm is proposed for the elbow motion to reach the palm towards the object of interest. EMG based system is capable of controlling the prosthetic elbow motion with available muscles' signals. EMG based system is fused with the IBVS system to perform elbow motions when an object is visible to the camera. Experiments are carried out using a virtual prosthesis to validate the proposed task planning method.

The use of underwater vehicle manipulator systems (UVMS) equipped with cameras has gained significant attention due to their capacity to perform underwater tasks autonomously. However, controlling both the manipulator and the remotely operated vehicle (ROV) based on the vision system information is not an easy task, especially in situations where the vehicle cannot be parked/held stationary. Most of the existing approaches work based on complex matrix calculations for the inverse kinematics (IK), which can lead to high computational costs and the need to deal with singularity problems. A problem arises when the amount of time needed to calculate the UVMS configuration can result in reduced frequency of target pose estimation, beyond the point where the target has moved out of the camera field of view. Therefore, this paper proposes an autonomous visual servoing approach for UVMS, including an extension of a heuristic technique named M-FABRIK (Mobile - Forward and Backward Reaching IK) to calculate the UVMS inverse kinematics in a simple and fast way. This approach aims to control both the configuration of the manipulator and ROV position in order to allow underwater intervention in situations where the ROV cannot be parked/held stationary. This solution allows the vehicle to be positioned according to additional criteria, besides avoiding matrix inversion and being robust to singularities. Trials have been performed with a manipulator mounted on a work-class ROV for an autonomous underwater monitoring task and results demonstrate a simple and fast approach, which is able to set the configuration of the manipulator as well as the ROV for visual servoing applications in real-time, such as for monitoring, tracking and intervention tasks underwater.

Image-based visual servoing (IBVS) can reach a desired position for a relatively stationary target using continuous visual feedback. Proper feature extraction and appropriate servoing control laws are essential to performance for IBVS. IBVS control can be interrupted or interfered abruptly if no features are extracted when the observed object is occluded. To address the problem of missing feature points in current images during a visual navigation task, a homography method that uses a priori visual information is proposed to predict all of the missing feature points and to ensure the execution of IBVS. The mixture parameter for the image Jacobian matrix can also affect the control of IBVS. The settings for the mixture parameter are heuristic so there is no a systematic approach for most IBVS applications. An adaptive control approach is proposed to determine the mixture parameter. The proposed method uses a reinforcement learning (RL) method to adaptively adjust the mixture parameter during the robot movement, which allows more efficient control than a constant parameter. A logarithmic interval state-space partition for RL is used to ensure efficient learning. The integrated visual servoing control system is validated by several experiments that involve wheeled mobile robots reaching a target with a desired configuration. The results for simulation and experiment demonstrate that the proposed method has a faster convergence rate than other methods.

A 2-1/2-D visual servoing strategy, which is based on a novel motion-estimation technique, is presented for the stabilization of a nonholonomic mobile robot (which is also called the “parking problem”). By taking into account the planar motion constraint of mobile robots, the proposed motion-estimation technique can be applied in both planar and nonplanar scenes. In addition, this approach requires no matrix estimation or decomposition, and it avoids ambiguity and degeneracy problems for the homography or fundamental matrix-based algorithms. Moreover, the field-of-view (FOV) constraint of the onboard camera is largely alleviated because the presented algorithm works well with few feature points. In order to incorporate the advantages of position-based visual servoing and image-based visual servoing, a composite error vector is defined that includes both image signals and the estimated rotational angle. Subsequently, a smooth time-varying feedback controller is adopted to cope with the nonholonomic constraints, which yields global exponential convergent rate for the closed-loop system. On the basis of the perturbed linear system theory, we show that practical exponential stability can be achieved, despite the lack of depth information, which is inherent for monocular camera systems. Both simulation and experiment results are collected to investigate the feasibility of the proposed approach.

The research on acceleration-level visual servoing of manipulators is crucial yet insufficient, which restricts the potential application range of visual servoing. To address this issue, this paper proposes a quadratic programming-based acceleration-level image-based visual servoing (AIVS) scheme, which considers joint constraints. Besides, aiming to address the unknown problems in visual servoing systems, a data-driven learning algorithm is proposed to facilitate estimating structural information. Building upon this foundation, a data-driven acceleration-level image-based visual servoing (DAIVS) scheme is proposed, integrating learning and control capabilities. Subsequently, a recurrent neural network (RNN) is developed to tackle the DAIVS scheme, followed by theoretical analyses substantiating its stability. Afterwards, simulations and experiments on a Franka Emika Panda manipulator with eye-in-hand structure and comparisons among the existing methods are provided. The obtained results demonstrate the feasibility and practicality of the proposed schemes and highlight the superior learning and control ability of the proposed RNN. This method is particularly well-suited for visual servoing applications of manipulators with unknown structure.