8. Robotics-Forum an der FH Dortmund

Robotic assistance systems for diagnosis and therapy have become technically mature and widely available. Thus, they play an increasingly important role in patient care. This talk provides an overview of the general concepts of robotically assisted surgical systems and discusses current focus areas of research. Furthermore, general challenges in this field will be highlighted and tight interdisciplinary partnerships of the main stakeholders, e.g. clinics, industry and academia will be motivated.

Julian Klodmann is a Robotics Researcher at the Institute of Robotics and Mechatronics (DLR) with more than 10 years experience in Surgical Robotics. He leads the Surgical Robotics team at DLR, which focusses on modular, collaborative robotically assisted surgical systems enabling an efficient transition towards robotic assisted surgery, providing adequate assistance to physicians. Finally, he is the scientific lead of the MIRO Innovation Lab, to drive innovation in surgical robotics together with clinics, industry and academia. His research focuses in particular on calibration and control aspects of torque controlled, flexible joint lightweight robots for surgical applications.

Deep neural networks (DNNs) have become an important tool in many fields of applications from image recognition to natural language processing and beyond, often outperforming human experts in their domains due to their better prediction accuracy and generalizability to unseen data.

In robotics, DNNs have been successfully applied to diverse tasks such as state estimation, path planning, and control for various platforms.

In this talk, we will explore how deep neural networks can solve difficult perception and localizations tasks for different applications in robotics such as drone-based inspection of critical infrastructure and localization of surgical tools and lesions in ultrasound-guided interventional robotics. We will also discuss how the predictions of deep neural networks can be combined with other sensor modalities in a classical sensor fusion framework.

Jan Steinbrener is an assistant professor on a tenure track position in the Control of Networked Systems group (CNS) at the University of Klagenfurt. He obtained his PhD in Physics in 2010 from Stony Brook University in Stony Brook, NY USA. After his PhD, he worked as a postdoctoral researcher at the Max-Planck Institute for medical research in Heidelberg, Germany, and then spent 5 years working in industry developing medical x-ray machines at Siemens Healthcare in Erlangen, Germany. Before joining CNS in 2019, he worked as a senior researcher at the Carinthian Tech Research Centre (now Silicon Austria Labs) in Villach Austria. His current research focuses on combining machine learning approaches with classical methods for state estimation and navigation of autonomous systems.

NASA’s CADRE (Cooperative Autonomous Distributed Robotic Exploration) project is developing a network of small rovers that will work together to explore the Moon in an experiment to demonstrate new technology. Communicating via mesh network radios with each other and a base station aboard a lunar lander, the rovers will be largely autonomous, making decisions and acting without the need for constant human intervention.

In this talk, we will have a closer look at the design of this team of autonomous explorers and the robotic capabilities that enable this mission which is slated to arrive at the Reiner Gamma region of the Moon aboard the commercial Intuitive Machines 3 (IM-3) lander in 2024 as part of the NASA’s CLPS (Commercial Lunar Payload Services) initiative

Roland Brockers is a Research Technologist at NASA’s Jet Propulsion Laboratory/California Institute of Technology with more than 20 years of R&TD experience in vision-based autonomous navigation of unmanned systems. He is currently the Surface Mobility lead on the CADRE mission. Roland received a Ph.D. in Electrical Engineering from the University of Paderborn, Germany (2005), and is also a Professor for Modular Robotics Systems at the University of Klagenfurt, Austria.