As manufacturers are automating their production lines the use of robots isincreasing, but this does not exclude the need from humans with their nifty hands.Robots are using a certain tool for a certain job while humans have their hands forall situations. Schunk has developed a hand for these robot arms but it has yet tobe tested and implemented in a real world situation. By using a robot arm acompany can minimize the risk of damage to humans.In this work we are exploring the options of teleoperation using ros and moveitby performing a pick-and-place scenario. This task is done by a UR10 equippedwith a Schunk SIH.The results were that a ROS node could control the robot hand by sendingcommands inline or through a publishing node. This node is compatible with thewhole system and may be operated alongside a UR10 robotic arm. Moveit'smotion planning tool using inverse kinematics can drive the UR10 robot. Therobot arm was able to accomplish a pick-and-place job by placing a water bottleon top of a box.This investigation analyzes this effort from a variety of angles, one of which isan ethical one that emphasizes the need of making these advances for the sake ofhuman health.

Digital twins in manufacturing, logistics, retail, and healthcare can help companies makebusiness decisions by simulating changes prior to implementing such changes in real life.In robotic teleoperation, virtual reality technology such as head mounted displays canincrease operator performance. In the mining equipment industry, teleoperation is quitean established concept, using a video feed for visualization, and often similiar or the samecontrol panels as on the real machine. However, cameras don’t provide depth perceptionfor the operator, and the lighting conditions in a mine may make photogrammetry a lessthan ideal solution. Epiroc is currently working on a digital twin simulation softwarein Unity, which could be extended for teleoperation purposes. As a complement to thissoftware, a fast, high-definition Ouster OS0-128 LiDAR was used to render a point cloudof a physical environment. A Unity GameObject script was written in C# that receivesand renders coordinates as a point cloud. Two Python scripts were written to convert theLiDAR data using the Ouster SDK to coordinates, and then sending these coordinates overa TCP connection, either on the same machine, or over Wi-Fi. The Python scripts used twodifferent data formats, and the performance difference between these two data formatswas compared. The results indicated that Wi-Fi transfer of LiDAR data could be a viablesolution to continously scanning the surrounding area of equipment being teleoperatedwith quite a low delay and latency

This project, completed in collaboration with Piktiv AB, aimed to develop an automated surface hole-filling feature for static meshes in Unreal Engine 4.27, with the goal of making repaired surfaces visually indistinguishable from their surrounding areas. The solution was primarily designed to address holes that arose from, but were not limited to, the use of Reduction Settings within Unreal Engine on static meshes. The functionality encompassed four key stages: boundary detection, where all holes on the mesh were identified; triangulation, which involved patching the hole using vertices from the boundary; refinement, entailing the addition of vertices and triangles to the patched area to mimic the density of the surrounding surface; and fairing, which smoothed the patched surface. Additionally, the project introduced a straightforward method for determining the texture coordinates of newly added vertices and a technique for ensuring that triangle normals correctly faced outward from the mesh. The Static Mesh Hole Filler, as implemented, demonstrates efficiency in filling an arbitrary amount of small, planar holes, which commonly result from polygon reduction using Reduction Settings in Unreal Engine. However, this function falls short in preserving unique texture details and maintaining the curvature of surfaces when dealing with larger holes. This limitation necessitates users to seek alternative methods for effectively repairing the mesh.

This project aims to contribute to the discussion regarding reproducibility of machinelearning research. This is done through utilizing the methods specified in the report ImprovingReproducibility in Machine Learning Research [30] to select an appropriateobject detection machine learning research paper for reproduction. Furthermore, this reportwill explain fundamental concepts of object detection. The chosen machine learningresearch paper, You Only Look Once (YOLO) [40] is then explained, implemented andtrained with various hyperparameters and pre-processing steps.While the reproduction did not achieve the results presented by the original machinelearning paper, some key insights were established. Firstly, the results of the projectdemonstrates the importance of pretraining. Secondly, the checklist provided by the NeurIPS[30] should be adjusted such that it is applicable in more situations.

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