Laser-based surgical ablation relies heavily on surgeon involvement, restricting precision to the limits of human error and perception. The interaction between laser and tissue is governed by various laser parameters that control the laser irradiance on the tissue, including the power, distance, spot size, orientation, and exposure time. This complex interaction lends itself to robotic automation, allowing the surgeon to focus on high-level tasks, such as choosing the region and method of ablation, while the lower-level ablation plan can be handled autonomously. This paper describes a sampling-based model predictive control (MPC) scheme to plan ablation sequences for arbitrary tissue volumes. Using a steady-state point ablation model to simulate a single laser-tissue interaction, a random search technique explores the reachable state space while preserving sensitive tissue regions. The sampled MPC strategy provides an ablation sequence that accounts for parameter uncertainty without violating constraints, such as avoiding nerve bundles.

This paper describes the safety and efficacy of an autonomous robotic system to collect ultrasound (US) images of the peripheral vasculature of 31 human participants, while also assessing their trust and comfort with the procedure. The procedure used a custom restraint mechanism and robotic arm guided by RGB-D imaging to collect clinically meaningful US images of human vasculature in the peripheral forearm safely and autonomously. All initial presses and scanned trajectories were executed under a safety force threshold (13N), included vasculature in imaging (from trajectory selected by non-clinician), and had a full scan completion success rate of greater than 80%. Participants indicated increased trust and perception of safety in the robotic system after the procedure. The positive findings suggest that careful attention to patient safety and well-designed patient/robot interactions can positively affect human-robot interaction and change the perception of robotic systems in medical contexts.