The purpose of this paper is to propose an error compensation method with error similarity analysis to improve the absolute positional accuracy of industrial robots. The positional error similarity is proposed with the analysis of the error model established by robot kinematic parameters, and is quantified with semivariogram function. Then an error compensation method is proposed base-on positional error similarity. The ordinary Kriging method is used to calculate the positional errors of the robot TCP. The error compensation is performed by modifying the position coordinates in the robot controlling commands. Experimental verification showed that the maximum positional error of the robot TCP was reduced by 75.36% from 1.2912mm to 0.3182mm. HighlightsThe positional errors of the industrial robot has similarity in the working space.The error similarity decreases as the distance between two positions increases.The proposed method doesn't need to identify or modify the kinematic parameters.The proposed method can effectively reduce the maximum positional errors of the robot.

Positional error similarity analysis for error compensation of industrial robots

This work investigates the potential use of a commercially-available portable photogrammetry system (the MaxSHOT 3D) in industrial robot calibration To demonstrate the effectiveness of this system, we take the approach of comparing the device with a laser tracker (the FARO laser tracker) by calibrating an industrial robot, with each device in turn, then comparing the obtained robot position accuracy after calibration As the use of a portable photogrammetry system in robot calibration is uncommon, this paper presents how to proceed It will cover the theory of robot calibration: the robot's forward and inverse kinematics, the elasto-geometrical model of the robot, the generation and ultimate selection of robot configurations to be measured, and the parameter identification Furthermore, an experimental comparison of the laser tracker and the MaxSHOT 3D is described The obtained results show that the FARO laser tracker ION performs slightly better: The absolute positional accuracy obtained with the laser tracker is 0365 mm and 0147 mm for the maximum and the mean position errors, respectively Nevertheless, the results obtained by using the MaxSHOT 3D are almost as good as those obtained by using the laser tracker: 0469 mm and 0197 mm for the maximum and the mean position errors, respectively Performances in distance accuracy, after calibration (ie maximum errors), are respectively 0329 mm and 0352 mm, for the laser tracker and the MaxSHOT 3D However, as the validation measurements were acquired with the laser tracker, bias favors this device Thus, we may conclude that the calibration performances of the two measurement devices are very similar

Robot calibration using a portable photogrammetry system

In this paper, an industrial robot is calibrated by identifying the nonlinear residual errors from massively measured tool positions using a laser tracker. A fully automatic measuring system is developed to collect more than 10,000 robot configurations in an efficient manner, which communicates with the robot controller and the laser tracker in real time. The robot configurations are programmed using a laser traceable path planning algorithm, which ensures the tool positions always locate in the expected envelope and the mirror ball keeps aligning against the laser tracker without occlusion. Afterwards, the robot motion control program is automatically generated using a robot simulation and off-line programming system. In order to obtain relatively higher positioning accuracy and less computation time, the off-line calibration procedure is subdivided into two steps. The first step solves the significant structural errors using the model-based parametric calibration. The second step further identifies the nonlinear residual errors using a deep neural network (DNN), so that the deviations related to different robot configurations can be reduced and the overall workspace accuracy can be improved to a much higher level. After calibration, the mean/maximum positioning errors in the measured envelope are reduced from 1.81 mm/1.96 mm, respectively, to 0.10 mm/0.22 mm. The calibrated model is eventually integrated into the off-line programming system, and effectively compensate a robot welding trajectory.

System identification of the nonlinear residual errors of an industrial robot using massive measurements

This study addresses the non-negligible flexibility problem of industrial robots and describes a rigid–flexible coupling error model for robot non-kinematic calibration. Simulation confirms that compliance errors are major sources of inaccuracies and affect robot accuracy with geometric errors. To reduce the impact of measurement noise and improve calibration performance, an enhanced approach is proposed for full pose measurement and identification optimisation. This approach is based on the self-adaption particle swarm optimisation (SAPSO) algorithm with a dual-index (i.e. observability index O1 and identification accuracy index Omin) and a modified Levenberg–Marquardt algorithm, which considers external constraints (i.e. structural interference and angular limitation). Simulation results illustrate that the 36 error parameters of the proposed error model can be identified with improved accuracy and stability using the proposed approach. Finally, experimental results obtained with a Staubli TX60L robot with a FARO laser tracker are introduced to demonstrate the practical effectiveness of our approach in robot accuracy improvement.

Non-kinematic calibration of industrial robots using a rigid–flexible coupling error model and a full pose measurement method

This paper presents an elasto-geometrical calibration method for improving the position accuracy of an industrial robot (ABB IRB 1600). Geometric parameter errors and joint stiffness parameters are identified through measuring the position of the robot's end-effector in several robot configurations using a laser tracker. Contrary to previous works, robot's position errors are measured under a wide range of external forces and torques for each robot configuration. A 6-DOF cable-driven parallel robot is employed to automatically apply the desired load on the end-effector of the ABB robot. Before the experiment, an observability analysis is performed in order to improve the robustness of the calibration process with respect to measurement noise and unmodeled errors. Accordingly, an optimal set of robot configurations and external loads is selected for the calibration process. The measured position errors of the ABB robot for this selected set are used to identify the real robot's elasto-geometrical parameters. Finally, the calibration efficiency is evaluated for a number of random combinations of robot configurations and external loads. The experimental results revealed that the proposed elasto-geometrical calibration approach is able to reduce the maximum position error to 0.960 mm, while a customary kinematic calibration can reduce the maximum position error only to 2.571 mm.

Elasto-geometrical calibration of an industrial robot under multidirectional external loads using a laser tracker

– The purpose of this paper is to describe a calibration method developed to improve the accuracy of a six degrees-of-freedom medical robot. The proposed calibration approach aims to enhance the robot’s accuracy in a specific target workspace. A comparison of five observability indices is also done to choose the most appropriate calibration robot configurations. , – The calibration method is based on the forward kinematic approach, which uses a nonlinear optimization model. The used experimental data are 84 end-effector positions, which are measured using a laser tracker. The calibration configurations are chosen through an observability analysis, while the validation after calibration is carried out in 336 positions within the target workspace. , – Simulations allowed finding the most appropriate observability index for choosing the optimal calibration configurations. They also showed the ability of our calibration model to identify most of the considered robot’s parameters, despite measurement errors. Experimental tests confirmed the simulation findings and showed that the robot’s mean position error is reduced from 3.992 mm before calibration to 0.387 mm after, and the maximum error is reduced from 5.957 to 0.851 mm. , – This paper presents a calibration method which makes it possible to accurately identify the kinematic errors for a novel medical robot. In addition, this paper presents a comparison between the five observability indices proposed in the literature. The proposed method might be applied to any industrial or medical robot similar to the robot studied in this paper.

Absolute accuracy analysis and improvement of a hybrid 6-DOF medical robot

The aim of this paper is to improve the position accuracy of a six degree of freedom medical robot. The improvement in accuracy is achieved without the use of any external measurement device. Instead, this work presents a novel calibration approach based on using an embedded force-torque sensor to identify the robot’s kinematic parameters and thereby enhance the positioning accuracy. A simulation study demonstrated that our calibration approach is effective, whether or not any measurement noise is present: the position error is improved, inside the robot target workspace, from 12 mm to 0.320 mm, for the maximum values, and from 9 mm to 0.2771 mm, for the mean errors.

https://www.mdpi.com/1424-8220/16/6/798/pdf

Use of a Force-Torque Sensor for Self-Calibration of a 6-DOF Medical Robot

In this work, we consider the problem of sensing environmental influence on the recognition of human intention in human robot interaction (HRI). We propose a novel framework to analyze the mechanism and the characteristics of the environment in all aspects of HRI. A sensor-based deep learning method is proposed to model the conditional probability of interaction between humans and robots in different environments. Having collected RGB-D videos with 3D human skeletons and environmental features by a Kinect azure RGBD sensor, people’s intentions to interact with a robot is inferred by a discriminant sensor network, trained jointly with a LSTM and a MLP. We collect a HRI dataset with various possible environments to train models and then conduct experiments of predicting the intention of participants. Our method outperforms the traditional environment-free approach in training results (98.8% and 77.0% in testing accuracy). Experimental validations of real-time human prediction also prove the higher speed and precision of our method compared with the baseline.

A Vision-Based Measure of Environmental Effects on Inferring Human Intention During Human Robot Interaction

Accurate morphological information on aortic valve cusps is critical in treatment planning. Image segmentation is necessary to acquire this information, but manual segmentation is tedious and time consuming. In this paper, we propose a fully automatic aortic valve cusps segmentation method from CT images by combining two deep neural networks, spatial configuration-Net for detecting anatomical landmarks and U-Net for segmentation of aortic valve components. A total of 258 CT volumes of end systolic and end diastolic phases, which include cases with and without severe calcifications, were collected and manually annotated for each aortic valve component. The collected CT volumes were split 6:2:2 for the training, validation and test steps, and our method was evaluated by five-fold cross validation. The segmentation was successful for all CT volumes with 69.26 s as mean processing time. For the segmentation results of the aortic root, the right-coronary cusp, the left-coronary cusp and the non-coronary cusp, mean Dice Coefficient were 0.95, 0.70, 0.69, and 0.67, respectively. There were strong correlations between measurement values automatically calculated based on the annotations and those based on the segmentation results. The results suggest that our method can be used to automatically obtain measurement values for aortic valve morphology.

/pdf/automatic-aortic-valve-cusps-segmentation-from-ct-images-1yndigbj.pdf

Long Fei Zhao

Papers

Absolute accuracy analysis and improvement of a hybrid 6-DOF medical robot

Use of a Force-Torque Sensor for Self-Calibration of a 6-DOF Medical Robot

A Vision-Based Measure of Environmental Effects on Inferring Human Intention During Human Robot Interaction

Automatic Aortic Valve Cusps Segmentation from CT Images Based on the Cascading Multiple Deep Neural Networks