Experience-Based Fuzzy Logic Framework for Robot-Assisted Endovascular Intervention

Robot-assisted intervention at a higher level of autonomy can reduce the surgical complexity and physician workload. However, as the core requirement, the autonomous delivery of the guidewire remains challenging. The inherent flexibility of the guidewire complicates physical modeling, while existing learning-based approaches require prolonged training and exhibit limited interpretability. To address these issues, this paper proposes a novel method, experience-based Fuzzy LOgic framework for Robot-assisted endovascular Intervention (FLORI). FLORI emulates clinical operator techniques by leveraging a fuzzy logic system. It dynamically computes control parameters by analyzing both real-time guidewire tip positioning within the vascular architecture and historical attempt data. The proposed method maintains interpretability while improving the success rate and efficiency of guidewire delivery. Furthermore, this paper introduces a “macro + micro" human-machine collaborative delivery paradigm that allows physicians to switch between autonomous and manual delivery modes, as well as modify the guidewire delivery path during the surgical procedure. Physical experiments on a coronary artery model have demonstrated the effectiveness of FLORI and the role of the human-machine collaborative paradigm in reducing physician workload and enhancing the success rate of surgical procedures.