Improving the Accuracy of Industrial Robots by offline Compensation of Joints Errors

The use of industrial robots in many fields of industry like prototyping, pre-machining and end milling is limited because of their poor accuracy. Robot joints are mainly responsible for this poor accuracy. The flexibility of robots joints and the kinematic errors in the transmission systems produce a significant error of position in the level of the end-effector. This paper presents these two types of joint errors. Identification methods are presented with experimental validation on a 6 axes industrial robot, STAUBLI RX 170 BH. An offline correction method used to improve the accuracy of this robot is validated experimentally

Improving robotic machining accuracy through experimental error investigation and modular compensation

Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis

Hadron detection with a dual-readout fiber calorimeter

In this paper, we describe measurements of the response functions of a fiber-based dual- readout calorimeter for pions, protons and multiparticle "jets" with energies in the range from 10 to 180 GeV. The calorimeter uses lead as absorber material and has a total mass of 1350 kg. It is complemented by leakage counters made of scintillating plastic, with a total mass of 500 kg. The effects of these leakage counters on the calorimeter performance are studied as well. In a separate section, we investigate and compare different methods to measure the energy resolution of a calorimeter. Using only the signals provided by the calorimeter, we demonstrate that our dual-readout calorimeter, calibrated with electrons, is able to reconstruct the energy of proton and pion beam particles to within a few percent at all energies. The fractional widths of the signal distributions for these particles (sigma/E) scale with the beam energy as 30%/sqrt(E), without any additional contributing terms

End-to-end Projector Photometric Compensation

Projector photometric compensation aims to modify a projector input image such that it can compensate for disturbance from the appearance of projection surface. In this paper, for the first time, we formulate the compensation problem as an end-to-end learning problem and propose a convolutional neural network, named CompenNet, to implicitly learn the complex compensation function. CompenNet consists of a UNet-like backbone network and an autoencoder subnet. Such architecture encourages rich multi-level interactions between the camera-captured projection surface image and the input image, and thus captures both photometric and environment information of the projection surface. In addition, the visual details and interaction information are carried to deeper layers along the multi-level skip convolution layers. The architecture is of particular importance for the projector compensation task, for which only a small training dataset is allowed in practice. Another contribution we make is a novel evaluation benchmark, which is independent of system setup and thus quantitatively verifiable. Such benchmark is not previously available, to our best knowledge, due to the fact that conventional evaluation requests the hardware system to actually project the final results. Our key idea, motivated from our end-to-end problem formulation, is to use a reasonable surrogate to avoid such projection process so as to be setup-independent. Our method is evaluated carefully on the benchmark, and the results show that our end-to-end learning solution outperforms state-of-the-arts both qualitatively and quantitatively by a significant margin.Comment: To appear in the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Source code and dataset are available at https://github.com/BingyaoHuang/compenne

CycleGAN for Interpretable Online EMT Compensation

Purpose: Electromagnetic Tracking (EMT) can partially replace X-ray guidance in minimally invasive procedures, reducing radiation in the OR. However, in this hybrid setting, EMT is disturbed by metallic distortion caused by the X-ray device. We plan to make hybrid navigation clinical reality to reduce radiation exposure for patients and surgeons, by compensating EMT error. Methods: Our online compensation strategy exploits cycle-consistent generative adversarial neural networks (CycleGAN). 3D positions are translated from various bedside environments to their bench equivalents. Domain-translated points are fine-tuned to reduce error in the bench domain. We evaluate our compensation approach in a phantom experiment. Results: Since the domain-translation approach maps distorted points to their lab equivalents, predictions are consistent among different C-arm environments. Error is successfully reduced in all evaluation environments. Our qualitative phantom experiment demonstrates that our approach generalizes well to an unseen C-arm environment. Conclusion: Adversarial, cycle-consistent training is an explicable, consistent and thus interpretable approach for online error compensation. Qualitative assessment of EMT error compensation gives a glimpse to the potential of our method for rotational error compensation.Comment: Conditionally accepted for publication in IJCARS & presentation at IPCA

Surface Location Error in Robotic Milling: Modeling and Experiments

Robotic milling offers new opportunities for discrete part manufacturing as an alternative to milling using large conventional machine tools. The advantage of industrial robots is their large work volume, configurability, and comparatively low cost. However, robots are significantly less stiff than conventional machine tools, which can lead to poor surface finish, low machining accuracy, and low material removal rates. The purpose of this research is to predict the geometric errors, or surface location errors, that occur in a robotic mulling tool path, validate these predictions with machining tests, and compensate these errors by tool path modification. Compared with conventional machine tools, robots possess low stiffness, low frequency vibration modes and the presence of these modes causes surface location errors that are nearly independent of spindle speed in the range typically used for machining. Additionally, the robot often exhibits errors relative to the commanded tool path. By developing an understanding of both the dynamics of the robot and its tool path accuracy, predictions were made of the surface location error for a machined part and a compensation algorithm was developed. The accuracy of the predictions and compensation algorithm were verified with a series of experiments. Through this research it was determined that robotic milling is prone to large surface location errors, but it is possible to reduce these through offline compensation

Thermal Performance of a Multi-Axis Smoothing Cell

Multi Axis Robots have traditionally been used in industry for pick and place, de-burring, and welding operations. Increasing technological advances have broadened their application and today robots are increasingly being used for higher precision applications in the medical and nuclear sectors. In order to use robots in such roles it is important to understand their performance. Thermal effects in machine tools are acknowledged to account for up to 70% of all errors (Bryan J. , 1990) and therefore need to be considered. This research investigates thermal influences on the accuracy and repeatability of a six degree of freedom robotic arm, which forms an integral part of a smoothing cell. The cell forms part of a process chain currently being developed for the processing of high accuracy freeform surfaces, intended for use on the next generation of ground based telescopes. The robot studied was a FANUC 710i/50 with a lapping spindle the end effector. The robot geometric motions were characterised and the structure was thermally mapped at the latter velocity. The thermal mapping identified the key areas of the robot structure requiring more detailed analysis. Further investigation looked into thermal variations in conjunction with geometric measurements in order to characterise the robot thermal performance. Results showed thermal variations of up to 13ºC over a period of six hours, these produced errors of up to 100μm over the 1300mm working stroke slow. Thermal modelling carried out predicted geometric variation of 70μm to 122μm for thermal variations up to 13ºC over a period of six hours. The modelling was 50% to 75% efficient in predicting thermal error magnitudes in the X axis. With the geometric and modelling data a recommendation for offline compensation would enable significant improvement in the robots positioning capability to be achieved

Misconceptions about Calorimetry

In the past 50 years, calorimeters have become the most important detectors in many particle physics experiments, especially experiments in colliding-beam accelerators at the energy frontier. In this paper, we describe and discuss a number of common misconceptions about these detectors, as well as the consequences of these misconceptions. We hope that it may serve as a useful source of information for young colleagues who want to familiarize themselves with these tricky instruments.Comment: Submitted to Instrument

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要約のみTohoku University高偉課