Optimizing Safety and Efficiency in the Suturing Task: A Comparison of Model Predictive Control and Control Barrier Function Framework

Robot Assisted Minimally Invasive Surgery (RAMIS) has revolutionized surgical practices by reducing pain, hospital stays, and complications. However, current surgical robots struggle with autonomously managing complex tasks due to strict safety requirements. The Model Predictive Control (MPC) and the Control Barrier Function (CBF) framework are emerged as valuable approaches to determine control variables while satisfying numerous constraints. In this work, we compare them in the autonomous execution of suturing task according to the medical guidelines. The test involves a da Vinci Research Kit and in the first phase the end-effect targets a goal pose while avoiding a self-collision with the other tools. In the second phase, a planar circular trajectory is imposed to minimize the tissue stress and the robot-human interaction. The comparison demonstrates the robustness and adaptability of MPC in the surgical environment while highlighting the optimization capabilities of the CBF framework in terms of safety and computational efficiency at each time-step.