Kinematic calibration of Orthoglide-type mechanisms from observation of parallel leg motions

The paper proposes a new calibration method for parallel manipulators that allows efficient identification of the joint offsets using observations of the manipulator leg parallelism with respect to the base surface. The method employs a simple and low-cost measuring system, which evaluates deviation of the leg location during motions that are assumed to preserve the leg parallelism for the nominal values of the manipulator parameters. Using the measured deviations, the developed algorithm estimates the joint offsets that are treated as the most essential parameters to be identified. The validity of the proposed calibration method and efficiency of the developed numerical algorithms are confirmed by experimental results. The sensitivity of the measurement methods and the calibration accuracy are also studied

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

Application of Machine Learning Methods to the Open-Loop Control of a Freeform Fabrication System

Freeform fabrication of complete functional devices requires the fabrication system to achieve well-controlled deposition of many materials with widely varying material properties. In a research setting, material preparation processes are not highly refined, causing batch property variation, and cost and time may prohibit accurate quantification of the relevant material properties, such as viscosity, elasticity, etc. for each batch. Closed-loop control based on the deposited material road is problematic due to the difficulty in non-contact measurement of the road geometry, so a labor-intensive calibration and open-loop control method is typically used. In the present work, k-Nearest Neighbor and Support Vector Machine (SVM) machine learning algorithms are applied to the problem of generating open-loop control parameters which produce desired deposited material road geometry from a description of a given material and tool configuration comprising a set of qualitative and quantitative attributes. Training data for the algorithms is generated in the course of ordinary use of the SFF system as the results of manual calibration of control parameters. Given the large instance space and the small training data set compiled thus far, the performance is quite promising, although still insufficient to allow complete automation of the calibration process. The SVM-based approach produces tolerable results when tested with materials not in the training data set. When control parameters produced by the learning algorithms are used as a starting point for manual calibration, significant operator time savings and material waste reduction may be achieved.Mechanical Engineerin

Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern

Line scanning cameras, which capture only a single line of pixels, have been increasingly used in ground based mobile or robotic platforms. In applications where it is advantageous to directly georeference the camera data to world coordinates, an accurate estimate of the camera's 6D pose is required. This paper focuses on the common case where a mobile platform is equipped with a rigidly mounted line scanning camera, whose pose is unknown, and a navigation system providing vehicle body pose estimates. We propose a novel method that estimates the camera's pose relative to the navigation system. The approach involves imaging and manually labelling a calibration pattern with distinctly identifiable points, triangulating these points from camera and navigation system data and reprojecting them in order to compute a likelihood, which is maximised to estimate the 6D camera pose. Additionally, a Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the uncertainty of the offset. Tested on two different platforms, the method was able to estimate the pose to within 0.06 m / 1.05$^{\circ}$ and 0.18 m / 2.39$^{\circ}$. We also propose several approaches to displaying and interpreting the 6D results in a human readable way.Comment: Published in MDPI Sensors, 30 October 201

A non-contact laser speckle sensor for the measurement of robotic tool speed

A non-contact speckle correlation sensor for the measurement of robotic tool speed is described that is capable of measuring the in-plane relative velocities between a robot end-effector and the workplace or other surface. The sensor performance has been assessed in the laboratory with sensor accuracies of ±0.01 mm/s over a ±70 mm/s velocity range. The effect of misalignment of the sensor on the robot was assessed for variation in both working distance and angular alignment with sensor accuracy maintained to within 0.025 mm/s (<0.04%) over a working distance variation of ±5 mm from the sensor design distance and ±0.4 mm/s (0.6%) for a misalignment of 5°. The sensor precision was found to be limited by the peak fitting accuracy used in the signal processing with peak errors of ±0.34 mm/s. Finally an example of the sensor’s application to robotic manufacturing is presented where the sensor was applied to tool speed measurement for path planning in the wire and arc additive manufacturing process using a KUKA KR150 L110/2 industrial robot

Development of a Robotic Positioning and Tracking System for a Research Laboratory

Measurement of residual stress using neutron or synchrotron diffraction relies on the accurate alignment of the sample in relation to the gauge volume of the instrument. Automatic sample alignment can be achieved using kinematic models of the positioning system provided the relevant kinematic parameters are known, or can be determined, to a suitable accuracy. The main problem addressed in this thesis is improving the repeatability and accuracy of the sample positioning for the strain scanning, through the use of techniques from robotic calibration theory to generate kinematic models of both off-the-shelf and custom-built positioning systems. The approach is illustrated using a positioning system in use on the ENGIN-X instrument at the UK’s ISIS pulsed neutron source comprising a traditional XYZΩ table augmented with a triple axis manipulator. Accuracies better than 100microns were achieved for this compound system. Although discussed here in terms of sample positioning systems these methods are entirely applicable to other moving instrument components such as beam shaping jaws and detectors. Several factors could lead to inaccurate positioning on a neutron or synchrotron diffractometer. It is therefore essential to validate the accuracy of positioning especially during experiments which require a high level of accuracy. In this thesis, a stereo camera system is developed to monitor the sample and other moving parts of the diffractometer. The camera metrology system is designed to measure the positions of retroreflective markers attached to any object that is being monitored. A fully automated camera calibration procedure is developed with an emphasis on accuracy. The potential accuracy of this system is demonstrated and problems that limit accuracy are discussed. It is anticipated that the camera system would be used to correct the positioning system when the error is minimal or notify the user of the error when it is significant