Hybrid force-position control during echographic vascular screening and implementation of a novel image-based sensing strategy for contact force estimation

Ultrasound imaging is a widely used diagnostic tool due to its safety, cost-effectiveness, and non-invasive nature. However, challenges such as the aging population, limited access to medical professionals, and the dependence on operator expertise significantly influence the quality and accessibility of diagnostic services. To address these issues, robotic ultrasound systems have been developed to provide autonomous, precise control of the ultrasound probe, improving image consistency while reducing the physical strain on sonographers. This thesis investigates force control in robotic ultrasound applications, with the aim of enhancing image quality by optimizing probe-tissue acoustic coupling. The study utilizes a six-degree-of-freedom Doosan robotic arm equipped with a linear ultrasound probe mounted on a custom support. The research explores two approaches: the implementation of a hybrid force-position control system and the development of an innovative force estimation method based on ultrasound data. Experimental results demonstrate that external pressure sensors outperform internal robotic sensors in maintaining consistent force during scanning procedures. This ensures improved acoustic coupling, reduces oscillations, and enhances the diagnostic quality of ultrasound images. To mitigate the visual occlusions caused by external sensors, the thesis introduces a novel method to estimate contact force directly from raw RF ultrasound data. This technique uses phase values to accurately estimate the applied force, achieving an error margin below one Newton, while preserving image clarity. The findings emphasize the critical role of precise force control in robotic ultrasound systems to ensure patient safety, improve image quality, and reduce artifacts. However, further calibration is required for nonlinear and organic tissues, which exhibit material-dependent behaviors under compression. This research lays the groundwork for future advancements like real-time feedback systems for probe alignment, and machine learning-based force estimation methods. These innovations have the potential to enable fully autonomous robotic ultrasound systems, offering reliable, high-quality diagnostics in complex clinical settings.