Position Of Robot End-effector Research Articles

Robot error compensation strategy based on error sensitivity

Abstract Due to the errors in manufacturing and assembly, there are differences between the actual model and the theoretical model of the robot, which affects the positioning accuracy of the robot end-effector. In order to improve the accuracy of robot end-effector position, a robot error compensation strategy based on error sensitivity is proposed.Firstly, the robot kinematic model is established by Denavit-Hartenberg (D-H) method, and the sensitivity of end-effector position error is analyzed. According to the influence degree of different kinematic parameters on the robot end-effector position accuracy in the whole workspace, different weights are given to different kinematic parameters. {\color{red}Secondly, the kinematic error model is established, and the redundancy of the error parameter matrix is analyzed to obtain an independent error model. Thirdly, based on the error sensitivity analysis, a Weighted Levenberg-Marquard (WLM) algorithm with adaptive penalty factor is proposed, and the kinematic parameters are iteratively identified.} Finally, an error compensation experiment is carried out by using a universal serial six-degree-of-freedom robot. The experimental results show that the maximum error, mean absolute error and root mean square error of the position error on the test set are reduced by 90.75%, 89.86% and 95.64% respectively. The research in this paper provides a theoretical basis for robot end error compensation.

Kinematic model calibration of a collaborative redundant robot using a closed kinematic chain

In this paper, we propose a novel approach for the kinematic calibration of collaborative redundat robots, focusing on improving their precision using a cost-effective and efficient method. We exploit the redundancy of the closed-loop kinematic chain by utilizing a spherical joint, enabling precise definition of the robot end-effector position while maintaining free joint motion in the null space. Leveraging the availability of joint torque sensors in most collaborative robots, we employ a kinesthetic approach to obtain constrained joint motion for calibration. An optimization approach is utilized to determine the optimal kinematic parameters based on measured joint positions and a constrained end-effector position defined by the spherical joint. The effectiveness of the proposed method is demonstrated and validated on the Franka Emika Panda robot, a 7-DoF robot. Results indicate a significant enhancement in absolute accuracy, with comparable performance to more expensive sensor systems such as optical measurement systems. Our approach offers a practical and cost-effective solution for improving the precision of collaborative robots.

Artificial Neural Networks for inverse kinematics problem in articulated robots

The inverse kinematics problem in articulated robots implies to obtain joint rotation angles using the robot end effector position and orientation tool. Unlike the problem of direct kinematics, in inverse kinematics there are no systematic methods for solving the problem. Moreover, solving the inverse kinematics problem is particularly complicated for certain morphologies of articulated robots. Machine learning techniques and, more specifically, artificial neural networks (ANNs) have been proposed in the scientific literature to solve this problem. However, there are some limitations in the performance of ANNs. In this study, different techniques that involve ANNs are proposed and analyzed. The results show that the proposed original bootstrap sampling and hybrid methods can substantially improve the performance of approaches that use only one ANN. Although all of these improvements do not solve completely the inverse kinematics problem in articulated robots, they do lay the foundations for the design and development of future more effective and efficient controllers. Therefore, the source code and documentation of this research are also publicly available to practitioners interested in adapting and improving these methods to any industrial robot or articulated robot.

Data-Driven Adaptive Modelling and Control for a Class of Discrete-Time Robotic Systems Based on a Generalized Jacobian Matrix Initialization

Data technology advances have increased in recent years, especially for robotic systems, in order to apply data-driven modelling and control computations by only considering the input and output signals’ relationship. For a data-driven modelling and control approach, the system is considered unknown. Thus, the initialization values of the system play an important role to obtain a suitable estimation. This paper presents a methodology to initialize a data-driven model using the pseudo-Jacobian matrix algorithm to estimate the model of a mobile manipulator robot. Once the model is obtained, a control law is proposed for the robot end-effector position tasks. To this end, a novel neuro-fuzzy network is proposed as a control law, which only needs to update one parameter to minimize the control error and avoids the chattering phenomenon. In addition, a general stability analysis guarantees the convergence of the estimation and control errors and the tuning of the closed-loop control design parameters. The simulations results validate the performance of the data-driven model and control.

Shared Control of Bimanual Robotic Limbs With a Brain-Machine Interface for Self-Feeding.

Advances in intelligent robotic systems and brain-machine interfaces (BMI) have helped restore functionality and independence to individuals living with sensorimotor deficits; however, tasks requiring bimanual coordination and fine manipulation continue to remain unsolved given the technical complexity of controlling multiple degrees of freedom (DOF) across multiple limbs in a coordinated way through a user input. To address this challenge, we implemented a collaborative shared control strategy to manipulate and coordinate two Modular Prosthetic Limbs (MPL) for performing a bimanual self-feeding task. A human participant with microelectrode arrays in sensorimotor brain regions provided commands to both MPLs to perform the self-feeding task, which included bimanual cutting. Motor commands were decoded from bilateral neural signals to control up to two DOFs on each MPL at a time. The shared control strategy enabled the participant to map his four-DOF control inputs, two per hand, to as many as 12 DOFs for specifying robot end effector position and orientation. Using neurally-driven shared control, the participant successfully and simultaneously controlled movements of both robotic limbs to cut and eat food in a complex bimanual self-feeding task. This demonstration of bimanual robotic system control via a BMI in collaboration with intelligent robot behavior has major implications for restoring complex movement behaviors for those living with sensorimotor deficits.

Reinforcement Learning With Vision-Proprioception Model for Robot Planar Pushing.

We propose a vision-proprioception model for planar object pushing, efficiently integrating all necessary information from the environment. A Variational Autoencoder (VAE) is used to extract compact representations from the task-relevant part of the image. With the real-time robot state obtained easily from the hardware system, we fuse the latent representations from the VAE and the robot end-effector position together as the state of a Markov Decision Process. We use Soft Actor-Critic to train the robot to push different objects from random initial poses to target positions in simulation. Hindsight Experience replay is applied during the training process to improve the sample efficiency. Experiments demonstrate that our algorithm achieves a pushing performance superior to a state-based baseline model that cannot be generalized to a different object and outperforms state-of-the-art policies which operate on raw image observations. At last, we verify that our trained model has a good generalization ability to unseen objects in the real world.

Delta robot joints control-based linear MPC controller

Controlling the joints' angles of a robot is an important step which lead to controlling the robot end effector position and/or speed. Thus, it has been a vast area of interest in research which has good investigating potentials using several control types such as classical, modern and optimal control methods. In this work, a linear model predictive control MPC technique was proposed to control the joints' angle of a three degree of freedom delta robot. The inverse kinematics, direct kinematics, and dynamic model of the robot were analysed. Then, the dynamic model represented in a linearised around an operating point state space model. In order to investigate the performance of the proposed MPC controller a simulator-based MATLAB program was implemented. The simulation results have showed the efficiency of the proposed controller in the joints' angles control problem. This illustrates that the MPC controller can derive the joints' angles to track the desired angles with invisible steady state error.

Real-Time Robot Pose Correction on Curved Surface Employing 6-Axis Force/Torque Sensor

Research on robot trajectory correction on a curved surface has seen a surge in the last two decades to deal with issues in many machining processes. Due to the intricacies involved in complex machining, the real-time correction of robot end-effector position and orientation is highly challenging. The existing approaches are either computationally expensive or inadequate for high accuracy in desired trajectory tracking. Hence, this research explores and implements a novel sophisticated approach, barring minor complications, to empower industrial robots to adjust poses in real-time automatically. In contrast with other studies, the proposed technique does not require any prior geometric information of the workpiece, such as a CAD model or a 3D scan. Our proposed technique relies solely on force/torque sensor feedback information obtained from a 6-axis force/torque sensor installed at the end-effector. When the developed tool comes in contact with the curved surface, the force sensor transmits the data to the pose correction algorithm. The pose correction algorithm estimates the adjustment required to make the tool normal to the surface. The effectiveness and feasibility of the proposed scheme were validated through numerous experiments carried out with a 6-DOF industrial robot. The final results of the curved surface trajectory correction demonstrates that the contact-based robot pose correction has significantly improved. The estimated average error on depth (Z-axis), and angles (Rx,Ry) are 0.7 <i>mm</i>, 0.7&#x00B0;, and 0.9&#x00B0;, respectively.

Robotic grasp manipulation using evolutionary computing and deep reinforcement learning

Intelligent object manipulation for grasping is a challenging problem for robots. Unlike robots, humans almost immediately know how to manipulate objects for grasping due to learning over the years. In this paper, we have developed learning-based pose estimation by decomposing the problem into both position and orientation learning. More specifically, for grasp position estimation, we explore three different methods such as genetic algorithm (GA)-based optimization method to minimize error between calculated image points and predicted end-effector (EE) position, a regression-based method (RM) where collected data points of robot EE and image points have been regressed with a linear model, a pseudoinverse (PI) model which has been formulated in the form of a mapping matrix with robot EE position and image points for several observations. Further for grasp orientation learning, we develop a deep reinforcement learning (DRL) model which we name as grasp deep Q-network (GDQN) and benchmarked our results with Modified VGG16 (MVGG16). Rigorous experimentation shows that due to inherent capability of producing very high-quality solutions for optimization problems and search problems, GA-based predictor performs much better than the other two models for position estimation. For orientation, learning results indicate that off policy learning through GDQN outperforms MVGG16, since GDQN architecture is specially made suitable for the reinforcement learning. Experimentation based on our proposed architectures and algorithms shows that the robot is capable of grasping nearly all rigid body objects having regular shapes.

Design and development of robotic end-effector position measuring device

Robot end-effector position measurement is very essential to determine the error between the commanded position and actual position reached by the robot end-effector. This also ensures the error with which the robot end-effector traces the commanded path. Conventionally position measuring devices like laser trackers, coordinate measuring machine and other non-contact-based position measuring devices are widely used to measure the position of the end-effector due to their high accuracy and precision, even though they are very expensive. This paper presents a cost effective solution for the measurement of position of robot end-effector. An end-effector position measuring device is designed and developed in spherical coordinate system using two absolute rotary encoders and a draw-wire sensor. A mathematical model for the end effector position measuring device is developed to determine the position of the end-effector with respect to a reference coordinate system using 3D homogeneous transformation approach. End-effector position measuring device is tested for ABB IRB 1600 for numerous poses and for a straight line path. The newly developed end-effector position measuring device is found to be capable of measuring the end effector position with an accuracy of 2 mm and hence can be implemented in calibration of industrial robots.

Identification of industrial robot frequency response function for robotic milling using operational modal analysis

Impact hammer experiments are typically used for identifying the Frequency Response Function (FRF) of six-degree-of-freedom (6-dof) industrial robots for machining applications. However, the modal properties of 6-dof industrial robots change as a function of robot arm configuration. Hence, describing the robot’s modal parameters within its workspace requires off-line impact hammer experiments performed at discrete robot end effector positions, which are costly and time consuming. Instead, it is more efficient to calculate the robot FRF using Operational Modal Analysis (OMA), a method that utilizes data acquired during the actual machining process. This paper presents an OMA approach to identify the robot FRF from measured milling forces and robot tool tip vibrations. Analysis of the milling process data reveal that periodic forces produced in the milling process are accompanied by background white noise that induce broadband excitation across the robot structure’s frequency spectrum. Hence, the tool tip vibration signal contains the signature of the structure’s free response that enables the use of OMA to estimate the robot’s FRF. The FRF calculated using OMA is shown to be in good agreement with results obtained from impact hammer experiments.

Design and development of robotic end-effector position measuring device

Robot end-effector position measurement is very essential to determine the error between the commanded position and actual position reached by the robot end-effector. This also ensures the error with which the robot end-effector traces the commanded path. Conventionally position measuring devices like laser trackers, coordinate measuring machine and other non-contact-based position measuring devices are widely used to measure the position of the end-effector due to their high accuracy and precision, even though they are very expensive. This paper presents a cost effective solution for the measurement of position of robot end-effector. An end-effector position measuring device is designed and developed in spherical coordinate system using two absolute rotary encoders and a draw-wire sensor. A mathematical model for the end effector position measuring device is developed to determine the position of the end-effector with respect to a reference coordinate system using 3D homogeneous transformation approach. End-effector position measuring device is tested for ABB IRB 1600 for numerous poses and for a straight line path. The newly developed end-effector position measuring device is found to be capable of measuring the end effector position with an accuracy of 2 mm and hence can be implemented in calibration of industrial robots.

A 3D Path Following Control Scheme for Robot Manipulators

Abstract We describe a novel path following control scheme for robot manipulators where constant tool velocity of travel on a surface is desirable. The path following scheme is applicable to general situations where the surface geometry is typically given in terms of measured data from a sensor. Considering the measured data points as control points, we utilize a cubic spline interpolation to generate a closed-form geometric description for the 3D path. Since joint velocity control is quite common in many industrial robots and most surface finishing tasks require travel with constant velocity along the path, we consider a 3D kinematic model for the end-effector with control inputs as rate of change of orientation and translational velocity that is locally tangent to the surface along the path. By utilizing a path variable and the tangent vector along the path, we formulate a converging path as the path that is traversed from a given robot end-effector position to the desired path and subsequent travel on the desired path on the surface. To evaluate the performance of the scheme, we have conducted a number of real-time experiments on a six degree-of-freedom industrial robot for several paths which can be employed for sanding of structures or deburring of large industrial cast parts and gears.

Computational Analysis of Kinematics of 3 – Links Articulated Robotic Manipulator

The Computational Analysis of Kinematics of 3 – Links Articulated Robotic Manipulator has been presented in this. The design of robot manipulators requires accurate computational analysis, involving the geometric position of the linking arms. The method of Forward Kinematics and Inverse Kinematics were employed in estimating the robotic arm’s position with respect to link lengths and angle, in which the angle required to move the end effector to a desired position is estimated and determined. A three link robotic arm with a rigid rotational base was also illustrated using free body diagrams, and computational estimation of the required parameters. The outcomes of the forward kinematics reveals that the robot end effector position can be estimated using the values of x, y, and z coordinates thereby providing a better means of controlling or adapting robot’s arm/motion to its environment.

Probabilistic Real-Time User Posture Tracking for Personalized Robot-Assisted Dressing

Robotic solutions to dressing assistance have the potential to provide tremendous support for elderly and disabled people. However, unexpected user movements may lead to dressing failures or even pose a risk to the user. Tracking such user movements with vision sensors is challenging due to severe visual occlusions created by the robot and clothes. In this paper, we propose a probabilistic tracking method using Bayesian networks in latent spaces, which fuses robot end-effector positions and force information to enable cameraless and real-time estimation of the user postures during dressing. The latent spaces are created before dressing by modeling the user movements with a Gaussian process latent variable model, taking the user's movement limitations into account. We introduce a robot-assisted dressing system that combines our tracking method with hierarchical multitask control to minimize the force between the user and the robot. The experimental results demonstrate the robustness and accuracy of our tracking method. The proposed method enables the Baxter robot to provide personalized dressing assistance in putting on a sleeveless jacket for users with (simulated) upper-body impairments.

Uncertainty Estimation of Robot Geometric Parameters and End-Effecter Position Based on New Generation GPS

The robot end-effecter positioning accuracy can be improved by the calibration of robot geometric parameters errors. According to the requirements of new generation geometrical product specification (GPS), the calibration uncertainty should be given when the calibration results are given. In this paper, the modified Denavit-Hartenberg method (MDH) of six-joint series robot is established and the joint movement trajectory method is applied to calibrate the robot geometric parameters. The uncertainty contributors significant are analyzed and the calibration uncertainty of robot geometric parameters is estimated based on the guide to the expression of uncertainty in measurement (GUM). In order to overcome the limitations of GUM for highly nonlinear model and reduce computational cost based on Monte Carlo Simulation (MCS) error estimation, an adaptive MCS (AMCS) is proposed to estimate the uncertainty distribution of robot end-effector position. Simulation and practical example are illustrated and the experiments results confirm that not only can the proposed method evaluate the calibration uncertainty of geometric parameters, but also the uncertainty distribution of end-effecter positions in the whole robot workspace can be estimated by AMCS in which the number of MCS trials can be selected adaptively and the quality of the numerical results can be controlled directly. The proposed method not only can evaluate the uncertainty of six-joint series robot geometric parameters and end-effecter position rapidly and accurately, but also can be popularized to the estimation of calibration uncertainty of other kinds of robot geometric parameters.

Modeling of flange-mounted force sensor frequency response function for inverse filtering of forces in robotic milling

Abstract Force/torque sensors have great potential for use in industrial robotic applications. However, the Frequency Response Function (FRF) of the robot flange-mounted sensor is influenced by the dynamics of the robot arm, which distorts the force sensor measurements. The sensor FRF is required for developing an inverse filter to compensate for the distortion. However, the sensor FRF changes as a function of the robot end effector position, and therefore must be accurately modeled. This paper proposes a Gaussian Process Regression (GPR) model-based approach to predict the FRF of a force sensor mounted on the end effector of a six degree-of-freedom (6-dof) industrial robot. The force sensor FRF as a function of the robot position in the workspace is characterized using impact hammer experiments. GPR models that predict the magnitude of a specific frequency bin are developed using the impact hammer results. The GPR modeling approach is evaluated using cross evaluation metrics. The predicted sensor FRFs are used for inverse filtering the milling forces measured by the flange force sensor during robotic milling tests. The filtered forces are shown to be in good agreement with forces measured by a ground-mounted quartz-based piezoelectric force dynamometer; specifically, the inverse filter lowers the maximum peak-to-peak error in the force measured by the flange sensor from 146 N to 25.4 N. Hence, the proposed methodology is shown to be valid for improving the accuracy of robot end effector-mounted force/torque sensors.

The influence of reference position deviation on industrial robots positioning precision

Due to industrial robot’s higher precision and better repeatability that human workforce, they have been chosen to replace human workload in all types of industrial activities. Due to the increasing positioning precision and repeatability needed by the industrial operations, it is necessary to use industrial robots that comply with the required technical specifications. For this reason, research on new methods to increase positioning accuracy is necessary. Some of the positioning errors are caused by the reference positioning deviations present in the industrial robot structure. Due to the systematic nature of reference position deviations, by entering the values in a compensation method, the positioning error is permanently minimized. In the paper are presented new methods of reference positioning deviations determination. In the paper is presented a mathematical model based on direct kinematic modeling method, used for calculating the position of the industrial robot end-effector. By applying these method’s, we can determine the reference positioning deviations and by introducing them into a specialized calculation system we can simulate the impact of these deviations. By applying these methods on a six axis articulated industrial robot and introducing the industrial robot’s parameters in a specialized calculating programs the positioning errors are determined and shown in the paper.

Independent load carrying and measurement manipulator robot arm for improved payload to mass ratio

Abstract Recently, the development of collaborative robots that have to work in a cooperative way with humans, has become an important trend both in academia and in the industry. Thereby, safety has increasingly become an essential research aspect. Today, the main techniques to ensure safety are based on reducing 1) the stiffness of the actuators and/or links, 2) the speed of the robot when it approaches humans or obstacles, and 3) the weight of the robot. In all these techniques, the robot's end-effector position is measured by position sensors on the joints, whereby the robot's limbs have to be as rigid as possible. This method has as possible drawbacks that the limbs have to carry unnecessary load (i.e. the robot's weight), and that the position errors increase with increased payload so that the robot can only be used for low payloads. To tackle these problems, we proposed a novel error compensation method based on the use of an additional measurement arm in parallel with the main load bearing arm, whereby the two arms are only coupled between the base and the end-effector. We designed a proof of concept robotic arm and validated the feasibility of our method. This paper presents the End-Effector Position Measuring (EEPM) method and introduces the Independent Load And Measurement Arm (ILAMA) to demonstrate the EEPM concept. With this novel method, the robot can be designed by strength instead of by stiffness. As a consequence, the weight of the limbs can drastically be reduced and the payload to mass ratio can be increased to a value that is bigger than one, while preserving the high end-effector position accuracy, as shown in the experiments. These advantages make the EEPM method very promising to use in collaborative robots or in mobile robot arms. Future works will investigate the feasibility of the proposed concept for real industrial robots with 6 to 7 DOF.

A Low-Cost Vision System in Determination of a Robot End-Effector's Positions

A Low-Cost Vision System in Determination of a Robot End-Effector's Positions