3D point cloud measurements of the surface of HFMI treated and untreated linear butt welds

The data consists of 3D point clouds of linear butt welds untreated or high-frequency mechanical impact (HFMI) treated. The scans have been acquired using a Wenglor MLWL131 profile sensor mounted onto a KUKA KR 60-3 industrial manipulator. The purpose of the data was to develop and validate an automated system for robotic weld toe treatment and quality assurance. A 3D surface representation of the weld allows for identifying the location of the weld toe, which can be used for automatically generating an adaptive robotic trajectory for treating the weld. Subsequently, the treated weld can be rescanned, and the quantitative quality measures (e.g., indentation depth and groove width) can automatically be determined for quality assurance. The welds have therefore been treated using three different approaches to compare the treatment quality: - Manual: The weld is treated manually by a human operator. - Robotic manual: The weld is treated using a robot that follows a manually programmed straight trajectory. - Robotic adaptive: The weld is treated using a robot that follows an automatically generated trajectory that is adapted to the inconsistency of the weld toe. The treatment is generated based on a point cloud of the weld. The data set consists of 36 scans, representing 50 mm sections of butt weld with two weld toes, all acquired using the same straight scanning trajectory and normal to the surface of the sample. 18 of these scans are of untreated weld, whereas the remaining 18 are HFMI treated welds. 6 scans of each treatment type: manual, robotic manual, and robotic adaptive.The data consists of 3D point clouds of linear butt welds untreated or high-frequency mechanical impact (HFMI) treated. The scans have been acquired using a Wenglor MLWL131 profile sensor mounted onto a KUKA KR 60-3 industrial manipulator. The purpose of the data was to develop and validate an automated system for robotic weld toe treatment and quality assurance. A 3D surface representation of the weld allows for identifying the location of the weld toe, which can be used for automatically generating an adaptive robotic trajectory for treating the weld. Subsequently, the treated weld can be rescanned, and the quantitative quality measures (e.g., indentation depth and groove width) can automatically be determined for quality assurance. The welds have therefore been treated using three different approaches to compare the treatment quality: - Manual: The weld is treated manually by a human operator. - Robotic manual: The weld is treated using a robot that follows a manually programmed straight trajectory. - Robotic adaptive: The weld is treated using a robot that follows an automatically generated trajectory that is adapted to the inconsistency of the weld toe. The treatment is generated based on a point cloud of the weld. The data set consists of 36 scans, representing 50 mm sections of butt weld with two weld toes, all acquired using the same straight scanning trajectory and normal to the surface of the sample. 18 of these scans are of untreated weld, whereas the remaining 18 are HFMI treated welds. 6 scans of each treatment type: manual, robotic manual, and robotic adaptive

Geometric measurement of the surface of a v-bend during multi-scan laser forming

The data describes the measurement of a v-bend shape formed during multi-scan laser forming. The purpose of the measurements was to determine the dynamic response during laser forming of a v-bend. A measurement scanner was used to measure the height of a line perpendicular to the heating scan line of a laser during laser forming. In order to estimate a surface, 105 samples were made with identical settings with the measurement scanner moved along the heating scan line between samples. A total of 21 positions along the heating scan line were measured. Each position was measured using 5 samples. Due to a memory problem, the measurement scanner could only measure about 3.12 seconds at a time. The measurement scanner is started slightly before each heating scan line starts. Furthermore, each heating scan line is split into its own text file in the data set

Compact purge system for fiber coupled THz-TDS systems

Compact purge system for fiber coupled THz-TDS system

Problem Instances for the Generalized Assignment Problem (GAP) with Resource-Independent Task Profits and Identical Resource Capacity

This dataset includes the problem instances generated for the Generalized Assignment Problem (GAP) with resource-independent task profits and identical resource capacity. We have set the four problem features - the number of tasks, the number of resources, the homogeneity of resources, and the relative capacity ratio of resources to total demands for tasks - with different levels for the features, resulting in 54 classes of the problem instances. For each class, 20 instances are generated, thus, a total of 1080 instances is included in the dataset. The main aim for this dataset creation was to test the performance of a solution algorithm to the target optimization problem and to characterize the performance of the algorithm as a function of the problem feature values. The findings along with the process could help a solution algorithm developer to understand the target optimization problem and thus design a solution algorithm with acceptable performance

Bilateral Human-Robot Control for Semi-Autonomous UAV Navigation

This video demonstrates the work towards a novel control architecture for UAV navigation. In general, UAVs are not easy to operate and skilled pilots are required for a good performance in manual flight. However, currently it is impossible to capture every possible situation an UAV could encounter in the autonomous control. To avoid overly complicated control, a semi-autonomous control approach can be used, so the drone is partly autonomously and partly manually piloted. The novelty of the approach presented here is in the way this semi-autonomy is defined. As the UAV regularly operates autonomously, it is not desirable to switch to manual control in dangerous procedures. Instead, a more supervisory method of control can be applied in which the UAV is always controlled by the onboard computer, but the boundaries of control are controlled by the operator. Whenever a situation requires bigger risks, the operator is informed requested by the drone for help, which he\she can offer by softening certain boundaries of the UAV. This video demonstrates the concept

Vision-IMU Based Collaborative Control of a Blind UAV

This video demonstrates the concept of two UAVs in which one UAV is ‘blind’, i.e. has no sensing methods other than its onboard IMU, whereas the other UAV has a proper knowledge of its position and orientation in space. By observing a marker attached to the blind UAV, the other UAV helps the blind UAV in controlling its absolute position in space. It does so by estimating the relative pose between the two and adding this to the absolute position estimate of the ‘non-blind’ UAV. The video shows a proof-of-concept for this approach, which could be utilized in cases where an aerial manipulator needs to interact closely with feature-poor environments

A Compact, 3D printable Purge System for Terahertz Spectroscopy

We present the design of a compact purge system designed for the TeraSmart terahertz spectrometer offered by Menlo Systems, but compatible with other spectrometers based on fiber coupled photoconductive antennas. While connected to the nitrogen (or dry air) source, the system effectively deals with water vapor absorption that is a well-known issue in terahertz spectroscopy limiting a signal-to-noise ratio. The system was designed for the lens-based version of the TeraSmart spectrometer arranged in a linear configuration for transmission measurements. However, the underlying idea can also be adapted to a reflection configuration or a mirror-based spectrometer with off-axis parabolic mirrors mounted in a cage system

Why Talk to People When You Can Talk to Robots? Far-Field Speaker Identification in the Wild

We present a speaker-aware robotic system which recognizes users by voice in realistic, noisy conditions, highlighting the potential of speaker identification to enrich industrial and social HRI. We approach this as a CNN-based audio classification task, with the particular aim of producing fast, reliable, and explainable predictions. Our method is evaluated on a challenging 6-speaker dataset collected "in the wild" and showcased in a manufacturing scenario, where a collaborative robot personalizes its responses and prevents non-authorized users from executing commands

Compliant Aerial Manipulators: Developing the New Generation of Aerial Robotic Workers

This video demonstrates the results found in the research of a new topic in aerial manipulation. We attempt to successfully collide on the environment with the UAV without crashing. This can be useful to robustly establish contact with the environment in realistic outdoor scenarios, where precise knowledge on the position of the drone might not always available. In the video three different experiments are shown. In all of these experiments, the manipulator arm (in this case a rotating rod) is in front of the drones center of mass. The first experiment shows the collision of the drone with the environment when the manipulator is rigidly connected to the drone. This causes a severe impact, which destabilizes the drone. It is simply too much energy for the drone to handle. In the second experiment the manipulator arm is connected to the drone via a spring-damper system to reduce the severeness of the impact. This shows significant improvement, but the drone is unable to maintain contact and bounces. The key to success in this work was to add a mechanical one-direction stop on the manipulator. This stop allows the arm to be pressed in during impact, but prevents the arm from releasing the energy afterwards. The third experiment shows how this works and demonstrates a beautiful smooth impact to achieve contact