Closed-loop Calibration Research Articles

A stepwise parameter identification strategy for probe-based robot on-machine measurement system calibration based on sensitivity analysis

The probe-based measurement presents an economical and efficient on-machine measurement (OMM) method. However, the low position accuracy of robot presents significant challenges for probe-based robot OMM. To improve the measurement accuracy of robot OMM system, this paper calibrates the geometric parameters of robot OMM system through a closed-loop calibration method with sphere constraint. First, the geometric error model for robot OMM system is established. Given the difficulty in model parameter identification, this paper defines a new index, the relative distance sensitivity index (RDSI), to reveal the contribution of model parameter to relative distance accuracy. Subsequently, sensitivity analysis is conducted on model parameters based on RDSI and model parameters are grouped according to the analysis results. In parameter identification, parameters are identified step by step according to the grouping results. Experiment results show that after calibration, the max distance measurement error of robot OMM system is reduced from 0.54 mm to 0.12 mm.

A closed-loop calibration method for serial robots based on position constraints and local area measurement

This paper presents a cost-effective and efficient closed-loop calibration method to enhance the absolute positioning accuracy of industrial robots. The method utilizes an economical and highly accurate set of simple measurement devices, including a device mounted on the robot's end effector and a spherical constraint device positioned within the robot's workspace. A novel local area measurement method is employed for the spherical constraint device, effectively converting position errors into the errors in the sphere center position of the constraint device. Furthermore, the error model considers both kinematic parameter deviations of the robot itself as well as those of the measurement device, along with non-kinematic errors caused by link self-weight. The closed-loop calibration model relies on position constraints, enabling identification and compensation of all error parameters using Levenberg Marquardt method after substituting measured data. The effectiveness of this proposed method is demonstrated through simulation and experimentation. In simulations, average position error reduces from 8.249 mm to 0.8384 mm, while absolute mean difference in sphere center positions obtained using our measurement equipment decreases from 1.206 mm to 0.1027 mm. Significant reductions in both position error and difference in sphere center position are indicated after calibration. This proves that the absolute mean value of the sphere center position difference can be used as the basis for judging the size of the robot's end position error. Experimental results show that absolute mean difference in sphere center position decreases from 0.4319 mm to 0.02869 mm, leading to substantial improvement in overall positioning accuracy.

A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator with Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration

A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator with Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration

Kinematic Calibration of an Industrial Manipulator without External Measurement Devices

This paper presents a practical approach to fully automated kinematic calibration of an industrial manipulator. The approach is based on the principle of plane constraint. The electrical signal is used to fix the moment of contact between the conductive tool and the flat surface. The measurement data are manipulator configurations (joint angles) at the moment of contact. A modification of the algorithm to deal with the scaling problem is also proposed. This approach provides both high calibration accuracy and lower cost of the experimental setup compared to coordinate measuring machines (CMMs), laser trackers, and vision systems. The article examines the impact of various methods of kinematic parameterization of manipulators: the Denavit – Hartenberg agreement (DH), product of exponentials (POE), as well as the complete and parametrically continuous model (CPC) on the calibration accuracy. A comparison is made of the open-loop and the proposed closed-loop calibration methods on the Puma 560 model known in the literature. POE parameters were converted to DH and CPC to compare accuracy after calibration based on these parameterizations. The method of computing POE-CPC transformation as a solution to a certain optimization problem is proposed. The problem of identifying geometric parameters in the presence of restrictions is solved by gradient optimization methods. Experiments have been carried out on an ABB IRB 1600 industrial manipulator with an installed conductive probe and an ABB IRBP A-500 robotic positioner with a conductive metal flat surface. A technique for indirectly checking the accuracy of calibration of kinematic parameters is proposed based on a study of the accuracy of manipulation when using these parameters.

A hybrid metaheuristic and computer vision approach to closed-loop calibration of fused deposition modeling 3D printers

Fused deposition modeling (FDM) is one of the most popular additive manufacturing (AM) technologies for reasons including its low cost and versatility. However, like many AM technologies, the FDM process is sensitive to changes in the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters. The experience required to efficiently calibrate a printer to a new feedstock acts as a barrier to entry. To enable greater accessibility to non-expert users, this paper presents the first system for autonomous calibration of low-cost FDM 3D printers that demonstrates optimizing process parameters for printing complex 3D models with submillimeter dimensional accuracy. Autonomous calibration is achieved by combining a computer vision-based quality analysis with a single-solution metaheuristic to efficiently search the parameter space. The system requires only a consumer-grade camera and computer capable of running modern 3D printing software and uses a calibration budget of just 30 g of filament (~ $1 USD). The results show that for several popular thermoplastic filaments, the system can autonomously calibrate a 3D printer to print complex 3D models with an average deviation in dimensional accuracy of 0.047 mm, which is more accurate than the 3D printer’s published tolerance of 0.1–0.4 mm.

Deep Gaussian mixture adaptive network for robust soft sensor modeling with a closed-loop calibration mechanism

Process drift leads to an out-of-distribution problem between historical training data and online deployment data, which deteriorates the performance of soft sensors. Unfortunately, few existing deep learning soft sensors have calibration mechanisms to deal with the process drift. To this end, this article proposes a robust Deep Gaussian Mixture Adaptive Network (DGMAN)-based soft sensor with a closed-loop calibration mechanism. First, a Gaussian Mixture Conditional Variational Autoencoder (GMCVAE) is presented, with the ability to capture the multimodality in industrial data. Afterwards, we propose a novel semi-supervised Gaussian mixture domain adaptation to perform conditional and marginal probability distribution alignment in the Gaussian mixture probabilistic latent space created by GMCVAE, using both labeled and unlabeled target samples. Theoretically, two kinds of process drift, including concept drift and virtual drift, could be alleviated by conditional distribution adaptation and marginal distribution adaptation, respectively. In addition, a new closed-loop calibration mechanism is deployed in a pre-training and fine-tuning fashion. Finally, robust analysis in theory and experimental results on the gas turbine dataset demonstrate that the proposed DGMAN-based soft sensor with robustness against process drift can maintain long-term validity in real industrial processes. Calibrated by only 100 labeled samples per year, the RMSE of DGMAN is 5.55 ± 0.32 after three years of deployment in the power plant, outperforming all comparison methods.

Robot-mounted sensing and local calibration for high-accuracy manufacturing

Positional accuracy is critically important for application of industrial robots in fixtureless precision manufacturing; thus, this paper presents a method for simultaneously calibrating the kinematic parameters of robot and robot-mounted measurement devices. In the modeling step, the Modified Denavit–Hartenberg parameters are extended to include the world and tool frames within a minimal parameter set using a systematic approach. Closed-loop calibration is performed using the data collected in the local target workspace using the robot-mounted measurement device. The presented modeling and calibration methods are applied to a case study in which a machining task is performed using a DENSO VS-6556 W industrial robot equipped with a cutting tool, force sensor, and laser profile scanner. In the case study, local models are calibrated for the laser and tool frames using common 1-2-3 blocks as calibration artifacts . The robot/laser model is identified by scanning the artifacts with a robot-mounted laser scanner , and the robot/tool model is calibrated by touching the artifacts with a robot-mounted force sensor. In this application, the robot/laser model is used to scan and register a sheet metal workpiece, and the robot/tool model is used to plan a tool path to machine the periphery. Positional accuracy of the tool path was evaluated to be ± 0.15 mm within the target workspace, which shows significant improvement compared with the mean error of 0.8 mm achieved by global calibration of the robot/tool model. • Simultaneous calibration of robot and extrinsic parameters of robot-mounted sensors. • Closed-loop calibration without external measurement devices. • Local calibration to improve accuracy within target workspace. • Systematic approach to include world and tool/sensor frame in MDH parameter set. • Case study for high-accuracy robotic machining application.

Fast Kinematic Re-Calibration for Industrial Robot Arms.

Accurate kinematic modelling is pivotal in the safe and reliable execution of both contact and non-contact robotic applications. The kinematic models provided by robot manufacturers are valid only under ideal conditions and it is necessary to account for the manufacturing errors, particularly the joint offsets introduced during the assembling stages, which is identified as the underlying problem for position inaccuracy in more than 90% of the situations. This work was motivated by a very practical need, namely the discrepancy in terms of end-effector kinematics as computed by factory-calibrated internal controller and the nominal kinematic model as per robot datasheet. Even though the problem of robot calibration is not new, the focus is generally on the deployment of external measurement devices (for open loop calibration) or mechanical fixtures (for closed loop calibration). On the other hand, we use the factory-calibrated controller as an ‘oracle’ for our fast-recalibration approach. This allows extracting calibrated intrinsic parameters (e.g., link lengths) otherwise not directly available from the ‘oracle’, for use in ad-hoc control strategies. In this process, we minimize the kinematic mismatch between the ideal and the factory-calibrated robot models for a Kinova Gen3 ultra-lightweight robot by compensating for the joint zero position error and the possible variations in the link lengths. Experimental analysis has been presented to validate the proposed method, followed by the error comparison between the calibrated and un-calibrated models over training and test sets.

Research on Efficient RGB LEDs Color Parameter Error Prediction and Its High Accurate Calibration Method

Aiming at the problem of whether the color difference exceeds the threshold when the RGB-LED is lit, and how to calibrate the deviation, this paper proposes an RGB LED chromaticity parameter prediction and calibration algorithm. This article is divided into two parts for the intelligent prediction and calibration method of RGB LED chromaticity parameters. The first is the color difference prediction part. First, combining the ideas of random forest and XGBoost, this paper designs a classification algorithm and named it RF-XGBoost. Use RF-XGBoost to predict whether the color difference exceeds 0.0055 when the RGB lamp lights up 30 colors. Compared with decision tree, random forest and XGBoost, RF-XGBoost performs better in recall rate through comparative experiment analysis. The second is the color correction part. Aiming at the problem of excessive color difference when RGB-LED is lit, this paper proposes the Cor-Calibration color calibration algorithm. The function relation between chromaticity coordinate and error model is established. The current error compensation model, automatic selection of calibration step and closed-loop calibration are used to calibrate the colors beyond the error. Compared with the existing calibration algorithm, after using the Cor-Calibration algorithm, the color pass rate in the color gamut increased from 91.25% to 99.79%. Experiments show that the RF-XGBoost algorithm and Cor-Calibration algorithm can achieve precise screening and high-precision calibration of RGB-LEDs.

Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot.

Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.

Camera Geometric Calibration Using Dynamic Single-Pixel Illumination With Deep Learning Networks

Traditional methods of geometric camera calibration (GCC) are based on angle measurements (AM) or diffractive optical elements (DOE). However, the AM-based approach has the low accuracy and reliability because the vibration of rotating mechanisms, i.e., turntables and angle measuring accuracy easily influence the calibration accuracy. The DOE failure easily occurs in the calibration process when some micro-apertures are blocked by dust particles. In this paper, a new method for the GCC by means of single pixel illumination in a deep neural network (DNN) is presented. A closed-loop calibration link is composed of a single stimulus input produced by a single pixel generator and a collimator, an uncalibrated camera, and a DNN. The dynamic single-pixel illumination forms the different stimulus input of the DNN at different stimulus time. The synaptic weights (the camera interior parameters) of multilayer perceptron are adjusted until the cost function is minimized. This method is able to avoid the aforementioned shortcoming of conventional calibration methods. This method can be especially used for on-ground calibration of remote sensing cameras but in principle also suitable for on-orbit GCC and other cameras.

Impedance Control Self-Calibration of a Collaborative Robot Using Kinematic Coupling

This paper presents a closed-loop calibration approach using impedance control. The process is managed by a data communication architecture based on open-source tools and designed for adaptability. The calibration procedure uses precision spheres and a kinematic coupling standard machine tool components, which are suitable for harsh industrial environments. As such, the required equipment is low cost (approximately group-separator = , 2000 [$]USD), robust, and is quick to set up, especially when compared to traditional calibration devices. As demonstrated through an experimental study and validated with a laser tracker, the absolute accuracy of the KUKA LBR iiwa robot was improved to a maximum error of 0 . 990 , representing a 58.4% improvement when compared to the nominal model. Further testing showed that a traditional calibration using a laser tracker only improved the maximum error by 58 μ m over the impedance control approach.

New air speed calibration system at NMIJ for air speeds up to 90 m/s

National Metrology Institute of Japan (NMIJ) has established a high air speed standard facility and has been providing a calibration service since 2015. The facility has an air speed range of 40 m/s to 90 m/s with a relative expanded uncertainty (k = 2) of 0.63%. The purpose of this primary standard is mainly to contribute to the improvement of meteorological observation research and to the evaluation of flow field inside a turbo machine and around a high speed vehicle. The reference air speed is derived from the national primary gas flowrate standard of Japan. A conversion device from flowrate to air speed is installed in the test line of the closed-loop calibration facility. The reference air speed at the nozzle exit of the conversion device is obtained by comparing the integral of the air speed profiles and the reference volume flowrate. The total pressure tube used as a transfer standard is then calibrated against the reference air speed at the center of the nozzle exit. The Eiffel-type wind tunnel, which is a working standard for the daily calibration service, is calibrated using this total pressure tube. The present paper describes the calibration system, the traceability chain, and an uncertainty analysis using a validation method.

A modified closed-loop auto frequency calibration technique for a 1.571-GHz integer-N PLL

ABSTRACTIn this paper, a modified closed-loop auto frequency calibration technique (MCL-AFC) is adopted in an integer-N phase-locked loop (PLL) for GPS-L1 application. The ignorance of circuit initial conditions setting in the closed-loop AFC may cause the start-up trap and long frequency calibration time. To solve these problems, the MCL-AFC technique is introduced. The process of MCL-AFC is listed below: first, initialisation process is only used for start-up of PLL; second, closed-loop voltage comparison process and open-loop switching process will take place alternately until optimum frequency control words are obtained. Tuning voltage searching range is reduced by half during the voltage comparison process since VCO’s tuning voltage is set to half of supply voltage through switching process. The MCL-AFC circuit is implemented in a 1-poly 6-metal 180 nm CMOS process and its chip area is 0.0167 mm2. The measured locked output frequency of the PLL is 1.571 GHz and the out-band phase noise is −131.9dBc/Hz at 1 MHz. The calibration time of PLL with MCL-AFC circuit is reduced to only 5µs while whole locking time is about 10.2µs.

Transient “Single-Fuel” RCCI Operation With Customized Pistons in a Light-Duty Multicylinder Engine

Reactivity controlled compression ignition (RCCI) combustion in a light-duty multicylinder engine (MCE) over transient operating conditions using fast response exhaust unburned hydrocarbon (UHC1), nitric oxide (NO), and particulate matter (PM) measurement instruments was investigated. RCCI has demonstrated improvements in efficiency along with low NOx and PM emissions by utilizing in-cylinder fuel blending, generally using two fuels with different reactivity in order to optimize stratification. In the present work, a “single-fuel” approach for RCCI combustion using port-injected gasoline and direct-injected gasoline mixed with a small amount of the cetane improver 2-ethylhexyl nitrate (EHN) was studied with custom designed, compression ratio (CR) of 13.75:1, pistons under transient conditions. The EHN volume percentage in the mixture for the direct-injected fuel was set at 3%. In an experimental investigation, comparisons were made to transient RCCI combustion operation with gasoline/diesel. The experiments were performed over a step load change from 1 to 4 bar brake mean effective pressure (BMEP) at constant 1500 rev/min on a General Motors (GM) Z19DTH 1.9-L diesel engine. The transients were conducted by changing the accelerator pedal command to provide a desired torque output with a DRIVVEN engine control unit (ECU) that replaced the original Bosch ECU. All relevant engine parameters are adjusted accordingly, based on 2D-tables. Previous to the transient engine operation, four steady-state points were used to obtain performance and emission values. Engine calibration at these four points, as well as the interpolation of the intermediate points, allowed for smooth operation during the instantaneous step changes. Differences between the steady-state and transient results indicate the complexity of transient operation and show the need for additional controls to minimize undesirable effects. The steady-state points were calibrated by modifying the fuel injection strategy (actual start of injection (aSOI) timing, port-fuel injection (PFI) fraction, etc.), exhaust gas recirculation (EGR), and rail pressure in order to obtain predefined values for the crank-angle at 50% of total heat release (CA50). Furthermore, emission targets (HC1 < 1500 ppmC3, NO < 10 ppm, filter smoke number (FSN)<0.1 with a maximum pressure rise rate (MPRR) < 10 bar/deg) and noise level targets (<95 dB) for RCCI combustion were maintained during the calibration and mapping. The tests were performed with a closed-loop (CL) calibration by using a next-cycle (NC) controller to adjust the PFI ratio of each cycle in order to reach the steady-state CA50 values in the table. The results show that single-fuel RCCI operation can be achieved, but requires significant alteration of the operating conditions, and NOx emissions were significantly elevated for gasoline/gasoline–EHN operation. While combustion phasing could not be matched, UHC1 emissions were at a similar level as for gasoline/diesel combustion. It is expected that the implementation of different injection strategies and boosted operation, combined with use of higher compression ratio pistons in order to compensate for the lower reactivity direct injection (DI) fuel, could raise the potential for improved performance.

Improving calibration accuracy of a vibration sensor through a closed loop measurement system

A sensor is a transducer whose purpose is to sense (that is, to detect) some characteristic of its environments. It detects events or changes in quantities and provides a corresponding output, generally as an electrical or optical signal. It has been widely used in mechanical engineering, aeronautics and astronautics engineering, industrial control, etc. Sensors play important roles in industries such as manufacturing, where all require the sensor to act as a reliable unit [1], [2]. In practical engineering applications, a sensor is often used for measuring vibrations, including acceleration, velocity, and displacement. It is the primary link in achieving conversion, processing, recording, and storage of information. Therefore, its performance directly influences the reliability and accuracy of the entire system. The calibration for vibration sensors, which need to ascertain sensitivity, frequency response characteristics, amplitude linearity, etc., are required before the measurement is made [3], [4]. Traditionally, an open loop device (in which the excitation signal is adjusted manually and the parameters are recorded manually) is used for sensor calibration. This type of manual calibration generates a large amount of work and active jamming, which results in errors. If a closed loop calibration system can be designed (i.e., the excitation signals are adjusted automatically by devices, and the real time data are sampled and analyzed), it will offer abundant measurement information and analysis methods which can help improve calibration accuracy and efficiency. In this article, we introduce such a closed loop calibration system for vibration sensors.

Simulating the human brain: Scientists have started various major projects to simulate and understand the brain, but many neuroscientists remain sceptical about their scope and aims.

Simulating the human brain: Scientists have started various major projects to simulate and understand the brain, but many neuroscientists remain sceptical about their scope and aims.

Non-kinematic calibration of a six-axis serial robot using planar constraints

This paper describes a non-kinematic calibration method developed to improve the accuracy of a six-axis serial robot, in a specific target workspace, using planar constraints. Simulation confirms that the stiffness of the robot, as well as its kinematic parameters, can be identified. An experimental validation shows that the robot's accuracy inside the target workspace is significantly enhanced by reducing the maximum distance errors from 1.321mm to 0.274mm. The experimental data are collected using a precision touch probe, which is mounted on the flange of a FANUC LR Mate 200iC industrial robot, and a high precision 9-in. granite cube. The calibration method makes use of a linear optimization model based on the closed-loop calibration approach using multi-planar constraints. A practical validation approach designed to reliably evaluate the robot's accuracy after calibration is also proposed.

Advantages of closed-loop calibration in intracortical brain–computer interfaces for people with tetraplegia

Objective. Brain–computer interfaces (BCIs) aim to provide a means for people with severe motor disabilities to control their environment directly with neural activity. In intracortical BCIs for people with tetraplegia, the decoder that maps neural activity to desired movements has typically been calibrated using ‘open-loop’ (OL) imagination of control while a cursor automatically moves to targets on a computer screen. However, because neural activity can vary across contexts, a decoder calibrated using OL data may not be optimal for ‘closed-loop’ (CL) neural control. Here, we tested whether CL calibration creates a better decoder than OL calibration even when all other factors that might influence performance are held constant, including the amount of data used for calibration and the amount of elapsed time between calibration and testing. Approach. Two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial performed a center-out-back task using an intracortical BCI, switching between decoders that had been calibrated on OL versus CL data. Main results. Even when all other variables were held constant, CL calibration improved neural control as well as the accuracy and strength of the tuning model. Updating the CL decoder using additional and more recent data resulted in further improvements. Significance. Differences in neural activity between OL and CL contexts contribute to the superiority of CL decoders, even prior to their additional ‘adaptive’ advantage. In the near future, CL decoder calibration may enable robust neural control without needing to pause ongoing, practical use of BCIs, an important step toward clinical utility.

Linear Calibration of Oxygen Detection in Coal Mine Explosion Monitoring

Mine fire occurrence, continues to be one of the most enduring problems faced by the mining industry. The purpose of this study is to assess strategies for detecting the most important ingredient in the coal mine gas explosion-proof monitoring-oxygen content, so as to make the best foresee and prediction to the mine fire. The oxygen concentration within atmosphere located in the area of the seal gas explosion hazard safety zone should be less than 8%. In accordance with the national standard, the mine requires oxygen detection accuracy of 1%. The performances of an oxygen content monitoring system is influenced by such four elements as gas line, sensor, signal conditioning and processing as well as terminal handling system, etc.. In this work, through the use of computer-controlled technology, the oxygen content monitoring system was first made a linear open-loop calibration based on hardware, then made a linear closed loop calibration based on software programming, and at last, the famous OriginPRO data analysis system was used to analyze and interpret the sampling data, a programmed planning and real-time information correction of linear closed-loop calibration based on software was achieved, so that an oxygen content monitoring sensitivity limit of less than 0.36% was obtained in the system.