Majid Roshanfar

Sarvin Ghiasi, Majid Roshanfar, Jake Barralet et al.

This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing minimum distance estimation between multi-arm manipulators and the associated collision-aware warning. By combining analytical modeling, real time simulation, and machine learning, the framework offers a robust solution for ensuring safe robotic operations. An analytical model was developed to estimate the minimum distances between robotic arms based on their joint configurations, offering theoretical calculations that serve as both a validation tool and a benchmark. To complement this, a 3D simulation environment was created to model two 7 DOF Kinova robotic arms (Kinova inc., Boisbriand, QC, Canada), generating a diverse dataset of configurations for distance estimation and collision warning. Using these insights, a deep residual neural network model was trained with joint configurations as inputs. On the held out validation set, the model achieves R2 = 0.940, RMSE = 42.0 mm, MAE = 28.7 mm, and a near zero mean bias, demonstrating strong predictive accuracy and consistent generalization across the workspace. The framework is intended as an early collision warning layer, where a warning is triggered when the predicted inter-arm distance falls below a 0.2 m threshold, which corresponds to a surface to surface clearance of approximately 50 mm given the Kinova Gen3 (Kinova inc., Boisbriand, QC, Canada) cross sectional radius. This work demonstrates the effectiveness of combining analytical modeling with machine learning to enhance the precision and reliability of multi-arm robotic systems.

Pedram Fekri, Majid Roshanfar, Samuel Barbeau et al.

Cardiac catheterization remains a cornerstone of minimally invasive interventions, yet it continues to rely heavily on manual operation. Despite advances in robotic platforms, existing systems are predominantly follow-leader in nature, requiring continuous physician input and lacking intelligent autonomy. This dependency contributes to operator fatigue, more radiation exposure, and variability in procedural outcomes. This work moves towards autonomous catheter navigation by introducing DINO-CVA, a multimodal goal-conditioned behavior cloning framework. The proposed model fuses visual observations and joystick kinematics into a joint embedding space, enabling policies that are both vision-aware and kinematic-aware. Actions are predicted autoregressively from expert demonstrations, with goal conditioning guiding navigation toward specified destinations. A robotic experimental setup with a synthetic vascular phantom was designed to collect multimodal datasets and evaluate performance. Results show that DINO-CVA achieves high accuracy in predicting actions, matching the performance of a kinematics-only baseline while additionally grounding predictions in the anatomical environment. These findings establish the feasibility of multimodal, goal-conditioned architectures for catheter navigation, representing an important step toward reducing operator dependency and improving the reliability of catheterbased therapies.