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.

As autonomous vehicles (AVs) become more common in real-world crossing scenarios and their automation levels continue to increase, implicit driver-pedestrian communication cannot be relied on for pedestrian safety. To address this, future autonomous vehicles must incorporate tools to explicitly convey their intentions to pedestrians. In this context, external human-machine interfaces (eHMIs) have been developed and studied in transportation settings.This study used video simulations to recreate typical real-world pedestrian crossing scenarios, where AVs approached either without eHMIs or with eHMIs displaying a yield signal. The responses were collected from 60 adult participants, including their intentions of crossing during the scenarios and other perceptions as pedestrians through a questionnaire. The findings indicated that respondents’ willingness to cross varied across scenarios, with the presence or absence of the eHMI playing a crucial role, particularly when an AV approached a zebra crossing. In this scenario, subjective norms and attitudes were identified as key factors influencing the intention to cross the road when the AV was equipped with an eHMI. Enhancing these factors, such as fostering more positive attitudes through education, providing guides for safe road crossing and accompanying pedestrians across the road, could reduce the time pedestrians take to decide to cross, potentially improving overall traffic efficiency.

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.

In this paper, we present an autonomous RGB-D and 2D ultrasound-guided robotic system for collecting 3D localized volumes of peripheral vessels. This compact design, with available commercial components, lends itself to platform utility throughout the human body. The fully integrated system works with force limits for future safety in human use. We propose a PID force controller for smooth and safe robot scanning following a priori 3D trajectory generated from a surface point cloud. System calibration is implemented to determine transformations among sensors, end-effector and robot base. A vascular localization pipeline that consists of detection and tracking is proposed to find the 3D vessel positions in real-time. Precision tests are performed with both predesignated and autonomously selected areas in an arm phantom. The average variance of the autonomously collected ultrasound images (to construct 3D volumes) between repeated tests is shown to be around 0.3 mm, similar to the theoretical spatial resolution a clinical ultrasound system. This fully integrated system demonstrates the capability of autonomous collection of peripheral vessels with built-in safety measures for future human testing.