Dr. Christian Zehetner is currently working as a Professor at the University of Applied Sciences Upper, Austria.
The demands on mechatronic products and systems are changing steadily, as well as the design and engineering processes. Looking into the past or into the future, we observe seemingly constant challenges in optimising products and processes: On the one hand, we want to increase quality, precision, robustness, versatility, adaptability, multidisciplinarity, automation, interconnection, integration, intelligence, etc. On the other hand, we want to reduce development time and costs, as well as resource consumption like materials, energy, CO2, etc. To achieve these goals, rapid changes of the design and engineering processes have taken place in the last decades, and further changes are expected for the upcoming ones. Consequently, a constant adaption of education is necessary, as well as an ever-better coordination of study programmes, research, and industrial application. Scope of this paper is first a review on the last two decades of Austrian industrial research projects in cooperation of universities, research institutes, and companies. Exemplarily, a successful application of model-based systems engineering is considered: By applying a digital twin addressing several fields of mechatronics, fully automatic lot-size-one production of sheet metal parts was realized. Based on the experience gained in many years of industrial research projects, the changes of design and engineering processes are discussed, as well as the evolution of mechatronics engineering education. Secondly, an outlook on expected future developments is given based on examples of running industrial research projects. Digitalization, IoT, industry 4.0, etc., require more holistic engineering and management processes. Systems engineering becomes more and more important. Nowadays, there is an enormous amount of software for almost every task in industrial process chains. However, frequently, the lack of or insufficient compatibility of the various software tools cause the most serious bottlenecks, resulting in inefficient processes. As a possible answer, a new virtual engineering platform is presented. The development of the latter has recently started at the University of Applied Sciences Upper Austria. Goal is to integrate open-source and commercial software tools used for design and engineering of mechatronic products and systems. Essential software components of this platforms are Version Control, Issue Tracking, Computer-Aided Design (CAD), Computer-Aided Engineering (CAE) including Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Discrete Event Simulation (DES) as well as Augmented and Virtual Reality (VR/AR). In the scope of an upcoming research project, we plan to apply this platform at an industrial partner to manage their engineering processes including regular product and production process audits. With regards to education, it also turned out that this virtual engineering platform is a perfect playground for our students who are contributing with their projects and theses. Consequently, our solution can be a platform for better integrating education, research, and industrial application
Omer Kurkutlu is a robotic enthusiast working as one of the youngest Robotics Software Engineer at Kontrolmatik Technologies and also a researcher who has developed a quadruped robot called OK1 Robot. His research is focused on dynamic motion planning and control of Quadruped Robots. He has also made research contributions to different gait state estimations for four-legged animals in order to develop quadruped robot locomotion. During his final year of graduation in India, his project, Quadruped Robot, was selected as one of the best final year projects of graduating students. In addition, He has worked in the industry to design and control the Collaborative Robots, where the scope was applied to gravity compensation, free drive, safety mode, and collision detection of the robot.
Mobile robots are often used to inspect an environment or move objects from one place to another. This is a crucial application of robots in office, military, hospital, and factory floor applications. The first issue affecting mobile robots is locomotion. Although their motion usually takes place in known, controlled environments like a factory, department stores, and so on, on other occasions they have to move in dangerous, delicate, and extreme environments. There are some instances whereby conventional wheeled robots are not the best choice. Wheeled robots cannot navigate well over obstacles, and this is the main drawback of this type, depending on the terrain, such as rocky terrain, sharp declines, or areas with low friction. Due to its geographical location, environmental hazards, etc, part of the earth’s landmass may be inaccessible to humans. Four-legged robots also referred to as quadruped, can have very sophisticated locomotion patterns and provide means of navigating on surfaces where it seems impossible for wheeled robots. This project is to develop a reliable solution that enables the implementation of stable and fast static/dynamic walking of quadruped robots on even and uneven terrain. The robot captures/mimics the mobility, autonomy, and speed of four-legged living creatures. The robot moves with different gait types and uses an imu-sensor embedded in it for detecting slope in terrain changes.
Dr. Chuxiong HU is the Associate Professor (Tenured), Ph.D. Supervisor Institute of Mechatronic Engineering, Department of Mechanical Engineering, Lee Shau Kee Building A819, Tsinghua University, Beijing 100084, China
To meet the progressively stringent demands for trajectory tracking performance in precision/ultra-precision industry, the research on advanced mechatronic motion control methods has attracted more and more attentions. Several performance-oriented motion control strategies represented by extended iterative learning control, gated recurrent units control, and real-time iterative compensation control, will be gradually introduced from an interesting evolving perspective. Tracking performances including tracking accuracy, extrapolation capability for non-repetitive trajectories, and disturbance rejection ability under different control strategies are experimentally verified on practical precision mechatronic motion stages. By combining quantitative experimental results with underlying control mechanism analysis, a comprehensive and reliable selection guidance of novel precision motion control methods can be provided for different practical industrial scenarios.
Nafiseh Mollaei holds a Ph.D. in Biomedical Engineering. She is an expert in a wide variety of AI algorithms like natural language processing and machine learning. As a part of her industry program, she has been working at Volkwagen Autoeuopa to predict occupational disorders in order to increase the productivity of the automotive sector. Besides, she has worked several articles in terms of the applicability of AI in this domain, such as subjects of Human_Centered Explanaible AI, Knowledge Discovery Exploratory based on association rules mining, and also Reinforcement Learning in Industry 5.0. Presently, she has holder of post doctoral fellowership in Biosensors in Baylor Collage of Medicine, USA. She is working on Center to Stream Healthcare in Place (C2SHIP).
Job rotation is a work organization strategy with increasing popularity, given its benefits for workers and companies, especially those working with manufacturing. This study proposes a formulation to help the team leader in an assembly line of the automotive industry to achieve job rotation schedules based on three major criteria: improve diversity, ensure homogeneity, and thus reduce exposure level. The formulation relied on a genetic algorithm, that took into consideration the biomechanical risk factors (EAWS), workers’ qualifications, and the organizational aspects of the assembly line. Moreover, the job rotation plan formulated by the genetic algorithm formulation was compared with the solution provided by the team leader in a real life-environment. The formulation proved to be a reliable solution to design job rotation plans for increasing diversity, decreasing exposure, and balancing homogeneity within workers, achieving better results in all of the outcomes when compared with the job rotation schedules created by the team leader. Additionally, this solution was less time-consuming for the team leader than a manual implementation. This study provides a much needed solution to the job rotation issue in the manufacturing industry, with the genetic algorithm taking less time and showing better results than the job rotations created by the team leaders.
Barak is a researcher in the fields of artificial intelligence and sensor fusion. Barak has authored several patents and articles that have been published in professional journals. He is the founder of ALMA Tech. LTD, an AI & advanced navigation company. He was with Qualcomm 2019-2020, where he mainly dealt with DSP and machine learning algorithms. Prior to that, he led the localization project at Autotalks. He received the M.Sc. (2018) and B.Sc. (2016) degrees in Aerospace Engineering, as also a B.A. in Economics and Management (2016, Cum Laude) from the Technion, Israel Institute of Technology. Barak is currently completing his Ph.D. at the University of Haifa, Israel.
Imagen you wake up in the morning and check your navigation app, only to discover that “it is searching for a network”. Imagen that while you are driving the navigation app is suddenly stops working. Can you still navigate?
The GPS’s positioning services is one of the important things we have access to every day, hour, and moment. You want to know where you are at all times, so you probably use a GPS. The system was originally limited to use by the United States military, where civilian use was allowed from the 80s.
Let’s focus only on car positioning. Today, many drivers use Google Maps, Apple Maps, Waze, and other navigation apps to determine the best route to their destination. All these navigation apps rely on GPS availability to calculate the car position anytime, anywhere.
ALMA Technologies Ltd. was established to find a robust alternative for car positioning without relying on GPS. ALMA develops a unique positioning system based on low-cost inertial sensors and revolutionary AI algorithms that can operate without GPS. The system is mainly based on learning the road terrain and cutting-edge map-matching technique only by processing the inertial sensors in a real-time fashion. The relationship between the driver, road, car, and the map is consistently processed through the unique AI algorithm, allowing ALMA to feed many features to their deep learning models.
The company is led by ex-Google, ex-Qualcomm, and ex-Elbit employees who tackled various sensors-based positioning problems. The key perspective is to allow any driver and car to know their position with a low-cost accelerometer and gyroscope that can be found in any smartphone at a low-grade level. ALMA doesn’t require any regulation standards, high-cost sensors, and installation. It is just a simple “plug and play” SDK, allowing car drivers to keep on track.
Today there are two main R&D projects developed in ALMA: indoor and outdoor positioning. Many automotive OEM and Tier 1 companies have been dealing with indoor parking lot positioning for almost a decade. Their approach is to use sensor fusion to allow autonomous driving in this tough environment. There, not only is GPS is unavailable, but also cameras, V2X communications, LiDAR, and other sensors have trouble operating indoor.
ALMA learns the geometric of the surface, matches the trajectory of the car to the parking lot map. ALMA unique AI-based engine provides a very accurate car speed allowing keeping the positioning with a relatively small error in a complicated parking lot with more than five floors, leading the driver to a specified spot, storing the parking position on the driver’s smartphone, and guiding the car to the right exit. Today, ALMA performs adjustments to enable indoor positioning and navigation for over 3M drivers in Israel.
ALMA also develops an outdoor positing solution for GPS-denied environments such as urban canyons and tunnels. There, the car driver can still have the positing parameters and keep navigating without connecting the GPS. Moreover, a hybrid mode in the developed SDK allows the use of the inertial sensors together with the GPS if available. This mode takes care of the quality of the GPS measurement in these environments, as it is insufficient for car drivers. Today, ALMA makes the last adjustments to allow more than 100M users all over Europe, with improved positioning services, which are much more accurate, continuous, and cost-effective.
Ihab Abu Ajamieh obtained his PhD from University of Toronto, March 2020 and did his postdoctoral research there until August 2020, where he worked on micro devices and robotic systems. He joined the Department of Mechanical and Mechatronics Engineering at Birzeit University as an Assistant Professor in August 2020.
Dr. Ihab research interests focus on developing innovative technologies and instruments for manipulating and characterizing cells, molecules, and nanomaterials, including developing vision-based control and automation strategies for the cell’s micromanipulation and microsurgical operations, in addition to microfabrication and microfluidic devices.
Recently, he started working on ingestible electrochemical devices, aiming to design, build, and implant sensors to collect data remotely from different areas, to monitor and diagnose different diseases and for drug delivery inside the body.
The recent growing interest in single cell biology demonstrates that micromanipulation and microsurgical advance strategies are required to carry out single cell surgical processes, such as the Preimplantation Genetic Diagnosis (PGD). To perform PGD, a sample from the inner material of the embryo is extracted using a microsurgical operation called embryo biopsy, in which the embryo is reoriented safely to a predefined desired orientation, required for the embryo outer membrane (Zona Pellucida ZP) perforation, to extract the material sample, and separate it from the embryo. Currently, embryologists manually perform the embryo biopsy steps. However, direct human involvement contributes to a significant negative impact on the process throughput and success rate. Growing demand for such advanced strategies mandates the development of automated systems to achieve high throughput with high success rates. Here I will talk about novel methods for the automation of the blastocyst embryo biopsy steps, using the conventional tools currently available in the research labs and the in vitro fertilization clinics (IVF), computer vision, and image-based control algorithms. This talk includes four main sections, each relating to the automation of the four main steps: embryo reorientation, ablates the embryo outer membrane, sample extraction, and separating the sample. However, the focus will be on the reorientation step, since it is the most important step.