Kalman Filter-Based Robot Manipulator Five-Degrees of Freedom Uncalibrated Vision Positioning

2014 ◽  
Vol 668-669 ◽  
pp. 347-351 ◽  
Author(s):  
Lang Liu ◽  
Niu Wang ◽  
Chu Zhong Yu ◽  
Da Tao Wang
Keyword(s):  
Kalman Filter ◽  
Visual Servoing ◽  
Degrees Of Freedom ◽  
Robot Manipulator ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Image Features ◽  
Image Space ◽  
Online Estimation ◽  
Vision Positioning

Robot manipulator position and posture control is a popular topic in the field of uncalibrated visual servoing, this paper presents a kalman filter-based robot manipulator five-degrees of freedom uncalibrated vision positioning method. In the case of the fixed binocular cameras and manipulator parameters are unknown; firstly, the specific point and angle image features information in the camera image space were selected in order to describe the relative pose relationship between robot manipulator ends and goals. Then, the kalman filter online estimation algorithm was applied to calculate image Jacobian matrix which is mapping relationship between image space to cartesian mission space, and vision controller was designed in the image plane realized robot manipulator five-degrees of freedom uncalibrated vision positioning control. Finally, Six-degrees of freedom robot manipulator’s five-degrees of freedom uncalibrated visual positioning Simulink model established in the Matlab environment, and the simulation result show that kalman filter online estimation method made the robot manipulator rapid convergence to the desired position and posture with high accuracy.

Concepts of Augmented Image Space and Transformed Feature Space for Efficient Visual Servoing of an “Eye-in-Hand Robot”

Robotica ◽  
1991 ◽  
Vol 9 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Won Jang ◽  
Kyungjin Kim ◽  
Myungjin Chung ◽  
Zeungnam Bien
Keyword(s):  
Visual Feedback ◽  
Visual Servoing ◽  
Industrial Robot ◽  
Robot Vision ◽  
Feature Space ◽  
Image Features ◽  
Image Space ◽  
Formal Definition ◽  
Computational Burden ◽  
Definition Of

SUMMARYFor efficient visual servoing of an “eye-in-hand” robot, the concepts of Augmented Image Space and Transformed Feature Space are presented in the paper. A formal definition of image features as functionals is given along with a technique to use defined image features for visual servoing. Compared with other known methods, the proposed concepts reduce the computational burden for visual feedback, and enhance the flexibility in describing the vision-based task. Simulations and real experiments demonstrate that the proposed concepts are useful and versatile tools for the industrial robot vision tasks, and thus the visual servoing problem can be dealt with more systematically.

scholarly journals IMU-Based Online Kinematic Calibration of Robot Manipulator

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Guanglong Du ◽  
Ping Zhang
Keyword(s):  
Kalman Filter ◽  
Robot Manipulator ◽  
Experimental Studies ◽  
Estimation Method ◽  
Kinematic Parameter ◽  
Corner Detection ◽  
Measurement Unit ◽  
Robot Calibration ◽  
Self Calibration ◽  
Calibration Methods

Robot calibration is a useful diagnostic method for improving the positioning accuracy in robot production and maintenance. An online robot self-calibration method based on inertial measurement unit (IMU) is presented in this paper. The method requires that the IMU is rigidly attached to the robot manipulator, which makes it possible to obtain the orientation of the manipulator with the orientation of the IMU in real time. This paper proposed an efficient approach which incorporates Factored Quaternion Algorithm (FQA) and Kalman Filter (KF) to estimate the orientation of the IMU. Then, an Extended Kalman Filter (EKF) is used to estimate kinematic parameter errors. Using this proposed orientation estimation method will result in improved reliability and accuracy in determining the orientation of the manipulator. Compared with the existing vision-based self-calibration methods, the great advantage of this method is that it does not need the complex steps, such as camera calibration, images capture, and corner detection, which make the robot calibration procedure more autonomous in a dynamic manufacturing environment. Experimental studies on a GOOGOL GRB3016 robot show that this method has better accuracy, convenience, and effectiveness than vision-based methods.

Image-based visual servoing using a set for multiple pin-in-hole assembly

2019 ◽  
Vol 40 (6) ◽  
pp. 819-831
Author(s):  
Chicheng Liu ◽  
Libin Song ◽  
Ken Chen ◽  
Jing Xu
Keyword(s):  
Adaptive Algorithm ◽  
Visual Servoing ◽  
Degrees Of Freedom ◽  
Image Features ◽  
Interaction Matrix ◽  
Content Type ◽  
Static Interaction ◽  
Alignment Task ◽  
Time Variations ◽  
Assembly Tasks

Purpose This paper aims to present an image-based visual servoing algorithm for a multiple pin-in-hole assembly. This paper also aims to avoid the matching and tracking of image features and the remaining robust against image defects. Design/methodology/approach The authors derive a novel model in the set space and design three image errors to control the 3 degrees of freedom (DOF) of a single-lug workpiece in the alignment task. Analytic computations of the interaction matrix that link the time variations of the image errors to the single-lug workpiece motions are performed. The authors introduce two approximate hypotheses so that the interaction matrix has a decoupled form, and an auto-adaptive algorithm is designed to estimate the interaction matrix. Findings Image-based visual servoing in the set space avoids the matching and tracking of image features, and these methods are not sensitive to image effects. The control law using the auto-adaptive algorithm is more efficient than that using a static interaction matrix. Simulations and real-world experiments are performed to demonstrate the effectiveness of the proposed algorithm. Originality/value This paper proposes a new visual servoing method to achieve pin-in-hole assembly tasks. The main advantage of this new approach is that it does not require tracking or matching of the image features, and its supplementary advantage is that it is not sensitive to image defects.

Position Sensorless Control Based on Augmented Extended Kalman Filter for Permanent Magnet Linear Synchronous Motor

2011 ◽  
Vol 88-89 ◽  
pp. 350-354
Author(s):  
Hua Cai Lu ◽  
Ming Jiang ◽  
Li Sheng Wei ◽  
Bing You Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sensorless Control ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Variable ◽  
Detailed Explanation ◽  
System States ◽  
Position Sensorless

In order to achieve position sensorless control for PMLSM drive system, speed and position of the motor must be estimated. A novel sensorless position and speed estimation algorithm was designed for PMLSM drive by measuring terminal voltages and currents. That was state augmented extended Kalman filter (AEKF) estimation method. The resistance of the motor was augmented to the state variable. Then, the speed, position and the resistance were estimated simultaneously through extended Kalman filter (EKF). The influence of the resistance on the state estimation results could be reduced. As well as giving a detailed explanation of the new algorithm, experimental results were presented. It shows that the AEKF is capable of estimating system states accurately and reliability, and the proposed sensorless control system has a good dynamic response.

scholarly journals Adaptive 3D Visual Servoing of a Scara Robot Manipulator with Unknown Dynamic and Vision System Parameters

Automation ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 127-140
Author(s):  
Jorge Antonio Sarapura ◽  
Flavio Roberti ◽  
Ricardo Carelli
Keyword(s):  
Visual Servoing ◽  
Degrees Of Freedom ◽  
Moving Objects ◽  
Robot Manipulator ◽  
Vision System ◽  
Robot Dynamics ◽  
Complete Control ◽  
Scara Robot ◽  
Dynamic Controller ◽  
Control Scheme

In the present work, we develop an adaptive dynamic controller based on monocular vision for the tracking of objects with a three-degrees of freedom (DOF) Scara robot manipulator. The main characteristic of the proposed control scheme is that it considers the robot dynamics, the depth of the moving object, and the mounting of the fixed camera to be unknown. The design of the control algorithm is based on an adaptive kinematic visual servo controller whose objective is the tracking of moving objects even with uncertainties in the parameters of the camera and its mounting. The design also includes a dynamic controller in cascade with the former one whose objective is to compensate the dynamics of the manipulator by generating the final control actions to the robot even with uncertainties in the parameters of its dynamic model. Using Lyapunov’s theory, we analyze the two proposed adaptive controllers for stability properties, and, through simulations, the performance of the complete control scheme is shown.

scholarly journals Estimation of Sideslip Angle Based on Extended Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yupeng Huang ◽  
Chunjiang Bao ◽  
Jian Wu ◽  
Yan Ma
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Test Field ◽  
Extended Kalman Filtering ◽  
Sideslip Angle ◽  
Filtering Algorithm ◽  
Limit Test ◽  
Kalman Filtering Algorithm

The sideslip angle plays an extremely important role in vehicle stability control, but the sideslip angle in production car cannot be obtained from sensor directly in consideration of the cost of the sensor; it is essential to estimate the sideslip angle indirectly by means of other vehicle motion parameters; therefore, an estimation algorithm with real-time performance and accuracy is critical. Traditional estimation method based on Kalman filter algorithm is correct in vehicle linear control area; however, on low adhesion road, vehicles have obvious nonlinear characteristics. In this paper, extended Kalman filtering algorithm had been put forward in consideration of the nonlinear characteristic of the tire and was verified by the Carsim and Simulink joint simulation, such as the simulation on the wet cement road and the ice and snow road with double lane change. To test and verify the effect of extended Kalman filtering estimation algorithm, the real vehicle test was carried out on the limit test field. The experimental results show that the accuracy of vehicle sideslip angle acquired by extended Kalman filtering algorithm is obviously higher than that acquired by Kalman filtering in the area of the nonlinearity.

scholarly journals Research on the Effectiveness of Different Estimation Algorithm on the Autonomous Orbit Determination of Lagrangian Navigation Constellation

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Youtao Gao ◽  
Junkang Chen ◽  
Bo Xu ◽  
Jianhua Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Fading Memory ◽  
Variance Matrix ◽  
Autonomous Orbit Determination ◽  
Navigation Constellation

The accuracy of autonomous orbit determination of Lagrangian navigation constellation will affect the navigation accuracy for the deep space probes. Because of the special dynamical characteristics of Lagrangian navigation satellite, the error caused by different estimation algorithm will cause totally different autonomous orbit determination accuracy. We apply the extended Kalman filter and the fading–memory filter to determinate the orbits of Lagrangian navigation satellites. The autonomous orbit determination errors are compared. The accuracy of autonomous orbit determination using fading-memory filter can improve 50% compared to the autonomous orbit determination accuracy using extended Kalman filter. We proposed an integrated Kalman fading filter to smooth the process of autonomous orbit determination and improve the accuracy of autonomous orbit determination. The square root extended Kalman filter is introduced to deal with the case of inaccurate initial error variance matrix. The simulations proved that the estimation method can affect the accuracy of autonomous orbit determination greatly.

scholarly journals Intelligent Spacecraft Visual GNC Architecture With the State-Of-the-Art AI Components for On-Orbit Manipulation

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhou Hao ◽  
R. B. Ashith Shyam ◽  
Arunkumar Rathinam ◽  
Yang Gao
Keyword(s):  
Decision Making ◽  
Pose Estimation ◽  
Degrees Of Freedom ◽  
State Of The Art ◽  
Robot Manipulator ◽  
Estimation Algorithm ◽  
Ground Control ◽  
Robot Arm ◽  
Wide Range ◽  
Human Operators

Conventional spacecraft Guidance, Navigation, and Control (GNC) architectures have been designed to receive and execute commands from ground control with minimal automation and autonomy onboard spacecraft. In contrast, Artificial Intelligence (AI)-based systems can allow real-time decision-making by considering system information that is difficult to model and incorporate in the conventional decision-making process involving ground control or human operators. With growing interests in on-orbit services with manipulation, the conventional GNC faces numerous challenges in adapting to a wide range of possible scenarios, such as removing unknown debris, potentially addressed using emerging AI-enabled robotic technologies. However, a complete paradigm shift may need years' efforts. As an intermediate solution, we introduce a novel visual GNC system with two state-of-the-art AI modules to replace the corresponding functions in the conventional GNC system for on-orbit manipulation. The AI components are as follows: (i) A Deep Learning (DL)-based pose estimation algorithm that can estimate a target's pose from two-dimensional images using a pre-trained neural network without requiring any prior information on the dynamics or state of the target. (ii) A technique for modeling and controlling space robot manipulator trajectories using probabilistic modeling and reproduction to previously unseen situations to avoid complex trajectory optimizations on board. This also minimizes the attitude disturbances of spacecraft induced on it due to the motion of the robot arm. This architecture uses a centralized camera network as the main sensor, and the trajectory learning module of the 7 degrees of freedom robotic arm is integrated into the GNC system. The intelligent visual GNC system is demonstrated by simulation of a conceptual mission—AISAT. The mission is a micro-satellite to carry out on-orbit manipulation around a non-cooperative CubeSat. The simulation shows how the GNC system works in discrete-time simulation with the control and trajectory planning are generated in Matlab/Simulink. The physics rendering engine, Eevee, renders the whole simulation to provide a graphic realism for the DL pose estimation. In the end, the testbeds developed to evaluate and demonstrate the GNC system are also introduced. The novel intelligent GNC system can be a stepping stone toward future fully autonomous orbital robot systems.

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