Hydraulically-actuated compliant revolute joint for medical robotic systems based on multimaterial additive manufacturing

IEEE International Conference on Robotics and Automation (ICRA), Montréal, Canada, janvier 2019 Research team : AV

Hydraulically-actuated compliant revolute joint for medical robotic systems based on multimaterial additive manufacturing

IEEE International Conference on Robotics and Automation (ICRA), Montréal, Canada, janvier 2019 Research team : AV

Automatisierung im OP

Seine Gesundheit einem Roboter anvertrauen? Der Gedanke ist für viele Menschen noch gewöhnungsbedürftig. Dabei können Maschinen in vielen Bereichen präziser, schneller und länger am Stück arbeiten als jeder Arzt. Was operative oder invasive Eingriffe angeht, kann man sagen: Hier ist die Automatisierungstechnik mithilfe neuer Instrumentensysteme in der Lage, die Grenzen des manuell Machbaren zu überwinden. Für Ingenieure und Ingenieurinnen eröffnen sich dadurch vollkommen neue Einsatzbereiche. Zusammen mit Ärzten und Ärztinnen sowie Kolleginnen und Kollegen aus Naturwissenschaft und BWL entwickeln sie Ideen und konkrete Lösungen für die Herausforderungen in einem Operationssaal, zum Beispiel im Bereich der Krebstherapie

Positionierungsvorrichtung, Positionierungssystem und Verfahren zur Positionierung eines Instrumentes

Die Erfindung betrifft eine Positionierungsvorrichtung, ein Positionierungssystem und ein Verfahren zur Positionierung eines Instruments an einem Objekt, insbesondere an einem Patienten. Ein Lichtmusterprojektor projiziert ein Lichtmuster auf das Objekt und eine Lichtsensoreinheit bestimmt eine Position und/oder eine Orientierung des Lichtmusters, so dass das Instrument mit einer Handhabungsvorrichtung zu einem vorgegebenen Ort bewegbar ist

Deep Reinforcement Learning for the Navigation of Neurovascular Catheters

Endovascular catheters are necessary for state-ofthe- art treatments of life-threatening and time-critical diseases like strokes and heart attacks. Navigating them through the vascular tree is a highly challenging task. We present our preliminary results for the autonomous control of a guidewire through a vessel phantom with the help of Deep Reinforcement Learning. We trained Deep-Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) agents on a simulated vessel phantom and evaluated the training performance. We also investigated the effect of the two enhancements Hindsight Experience Replay (HER) and Human Demonstration (HD) on the training speed of our agents. The results show that the agents are capable of learning to navigate a guidewire from a random start point in the vessel phantom to a random goal. This is achieved with an average success rate of 86.5% for DQN and 89.6% for DDPG. The use of HER and HD significantly increases the training speed. The results are promising and future research should address more complex vessel phantoms and the use of a combination of guidewire and catheter

Deep Reinforcement Learning for the Navigation of Neurovascular Catheters

Endovascular catheters are necessary for state-of-the-art treatments of life-threatening and time-critical diseases like strokes and heart attacks. Navigating them through the vascular tree is a highly challenging task. We present our preliminary results for the autonomous control of a guidewire through a vessel phantom with the help of Deep Reinforcement Learning. We trained Deep-Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) agents on a simulated vessel phantom and evaluated the training performance. We also investigated the effect of the two enhancements Hindsight Experience Replay (HER) and Human Demonstration (HD) on the training speed of our agents. The results show that the agents are capable of learning to navigate a guidewire from a random start point in the vessel phantom to a random goal. This is achieved with an average success rate of 86.5% for DQN and 89.6% for DDPG. The use of HER and HD significantly increases the training speed. The results are promising and future research should address more complex vessel phantoms and the use of a combination of guidewire and catheter

ALICE - Artificial Intelligence Catheter. Towards Autonomous Closed Loop Control of Passive Endovascular Catheters Based on Deep Reinforcement Learning: Poster presented at Emerging Learning Techniques for Robotics, Workshop at the Hamlyn Symposium on Medical Robotics, 26th June 2019, London

Endovascular catheters are used for state of the art therapies of many widespread diseases. Navigating them can be very laborious and so far no robotic assistance exists for passive catheters. Steer-able catheters exist, but due to their large diameter they are not suitable for many interventions. We propose a closed loop control system where a deep reinforcement learning based control algorithm steers the catheter. The algorithm is provided with live data by a tracking system. Prior to the intervention the control algorithm is trained on the simulation model and by expert demonstration. Here we present the results of our experiments, where a control algorithm learns to steer a guidewire through a simplified vascular tree. Learning is performed in the simulation model and the result transferred to the test bench. Our results show that the algorithm is able to learn catheter steering. However the simulation results cannot be transferred to the test bench directly without facing a reduced accuracy due to the test bench not having perfect states like the simulation