The research and development of intelligent automation solutions is a ground-breaking point for the factory of the future. A promising and challenging mission is the use of autonomous robot systems to automate tasks in the field of maintenance. For this purpose, the robot system must be able to plan autonomously the different manipulation tasks and the corresponding paths. Basic requirements are the development of algorithms with a low computational complexity and the possibility to deal with environmental uncertainties. In this paper, an approach is presented, which is especially suited to solve the problem of maintenance automation. For this purpose, offline data from CAD is combined with online data from an RGBD vision system via a probabilistic filter, to compensate uncertainties from offline data. For planning the different tasks, a method is explained, which uses a symbolic description, founded on a novel sampling-based method to compute the disassembly space. For path planning, we use global state-of-the-art algorithms with a method that allows the adaption of the exploration stepsize in order to reduce the planning time. Every method is experimentally validated and discussed.

Automating complex tasks using robotic systems requires skills for planning, control and execution. This paper proposes a complete robotic system for maintenance automation, which can automate disassembly and assembly operations under environmental uncertainties (e.g. deviations between prior plan information). The cognition of the robotic system is based on a planning approach (using CAD and RGBD data) and includes a method to interpret a symbolic plan and transform it to a set of executable robot instructions. The complete system is experimentally evaluated using real-world applications. This work shows the first step to transfer these theoretical results into a practical robotic solution.

Synthetic image data generation has gained popularity in computer vision and machine learning in recent years. The work introduces a technique for creating artificial image data by utilizing 3D files and rendering methods in Python and Blender. The technique employs BlenderProc, a rendering tool for generating artificial images, to efficiently create a substantial amount of data. The output of the method is saved in JSON format, containing COCO annotations of objects in the images, facilitating seamless integration with current machine-learning pipelines. The paper shows that the created synthetic data can be used to enhance object data during simulation. The method can enhance the accuracy and robustness of machine-learning models by modifying simulation parameters like lighting, camera position, and object orientation to create a variety of images. This is especially beneficial for applications that require significant amounts of labelled real-world data, which can be time-consuming and labour-intensive to obtain. The study addresses the constraints and potential prejudices of creating synthetic data, emphasizing the significance of verifying and assessing the generated data prior to its utilization in machine learning models. Synthetic data generation can be a valuable tool for improving the efficiency and effectiveness of machine learning and computer vision applications. However, it is crucial to thoroughly assess the potential limitations and biases of the generated data. This paper emphasizes the potential of synthetic data generation to enhance the accuracy and resilience of machine learning models, especially in scenarios with limited access to labelled real-world data. This paper introduces a method that efficiently produces substantial amounts of synthetic image data with COCO annotations, serving as a valuable resource for professionals in computer vision and machine learning.

Within the scope of the development of the project, the acceleration and gyro signal, VR implementation, and User Control for VR were realized. The starting point was a list of MPU9250 data, which are acceleration and gyro data. The goal is to do the signal processing of the signal that is got from MPU9250 and implement the data with a specific format so that the LINAK LA36 actuator can be controlled in such a way that it performs as desired position. For this purpose, a multi-axis acceleration and position sensor is mounted on a surfing board which then collects the acceleration and Gyro data. A MATLAB Program had been written to convert the desired position into a CAN message. Before that, the raw acceleration data and gyro must be converted and filtered into actual data. First, the acceleration data was collected from another group. A signal processing method has been implemented. This includes gravitational force filter, noise filter, integration, and complementary filter method was used in order to get the accurate actual data that is able to be implemented into the system. For those methods, it was necessary to deal extensively with the characteristics of signal processing. VR application was implemented in this project to give the user a sensational surfing experience in the virtual world. User control has been implemented as well to allow users to control the video. Unity with Arduino work together so that both Arduino and Unity can communicate with each other, and signal messages will be sent to the Arduino from Unity and vice versa to achieve the synchronization of the video signal and Unity VR application.

Macro-micro manipulators combine a macro manipulator with a large workspace, such as an industrial robot, with a lightweight, high-bandwidth micro manipulator. This enables highly dynamic interaction control while preserving the wide workspace of the robot. Traditionally, position control is assigned to the macro manipulator, while the micro manipulator handles the interaction with the environment, limiting the achievable interaction control bandwidth. To solve this, we propose a novel control architecture that incorporates the macro manipulator into the active interaction control. This leads to a increase in control bandwidth by a factor of 2.1 compared to the state of the art architecture, based on the leader-follower approach and factor 12.5 compared to traditional robot-based force control. Further we propose surrogate models for a more efficient controller design and easy adaptation to hardware changes. We validate our approach by comparing it against the other control schemes in different experiments, like collision with an object, following a force trajectory and industrial assembly tasks.

Gripper finger design is a complex process that requires a lot of experience, time, and effort. For this reason, automating this design process is an important area of research that has the potential to improve the efficiency and effectiveness of robotic systems. The current approaches are aimed at the automated design of monolithic gripper fingers, which have to be manufactured additively or by machining. This paper describes a novel approach for the automated design of gripper fingers. The motivation for this work stems from the increasing demand for flexible, adaptable handling systems in various industries in response to the increasing individualization of products as well as the increasing volatility in the markets. Based on the CAD data of the handling objects, the most suitable configuration of gripper fingers can be determined from the existing modules of a construction kit for the respective handling object, which can significantly reduce the provisioning time for new gripper fingers. It can be shown that gripper fingers can be effectively configured for a variety of objects and two different grippers, increasing flexibility in industrial handling processes.