Julian Klodmann

Alistair Weld, Michael Dyck, Julian Klodmann et al.

The aim of this paper is to introduce a robotic platform for autonomous iUS tissue scanning to optimise intraoperative diagnosis and improve surgical resection during robot-assisted operations. To guide anatomy specific robotic scanning and generate a representation of the robot task space, fast and accurate techniques for the recovery of 3D morphological structures of the surgical cavity are developed. The prototypic DLR MIRO surgical robotic arm is used to control the applied force and the in-plane motion of the US transducer. A key application of the proposed platform is the scanning of brain tissue to guide tumour resection.

Dario Onfiani, Michael Dyck, Luigi Biagiotti et al.

In this paper, we address the development of an adaptive assistance system for robot-assisted laparoscopic surgery, specifically for delicate tasks such as Resection and Dissection. Even if Virtual Fixtures offer significant advantages for guiding a surgeon's movements, conventional Virtual Fixtures are often defined by fixed geometries, lacking the flexibility to adapt to the surgical workflow or the surgeon's immediate intent. To address these limitations, we propose a novel framework for an adaptive and anisotropic virtual fixture. In addition, we introduce an intuitive control interface that modulates the fixture's geometry in real-time based on the surgeon's intent, inferred from EMG signals. This approach allows the surgeon to dynamically expand or disengage the constraint by contracting their forearm muscles, enabling seamless transitions between precise guided motion and free repositioning of the tool. Experimental results from a pilot user study, based on a standardized surgical training task, demonstrate the effectiveness of the proposed method. The system showed significant improvements in task accuracy and movement consistency, alongside a reduction in perceived cognitive load, effort, and frustration.