Multi-Modal Interaction Control of Ultrasound Scanning Robots with Safe Human Guidance and Contact Recovery

Ultrasound scanning robots enable the automatic imaging of a patient's internal organs by maintaining close contact between the ultrasound probe and the patient's body during a scanning procedure. Comprehensive, high-quality ultrasound scans are essential for providing the patient with an accurate diagnosis and effective treatment plan. An ultrasound scanning robot usually works in a doctor-robot co-existing environment, hence both efficiency and safety during the collaboration should be considered. In this paper, we propose a novel multi-modal control scheme for ultrasound scanning robots, in which three interaction modes are integrated into a single control input. Specifically, the scanning mode drives the robot to track a time-varying trajectory on the patient's body under the desired impedance model; the recovery mode allows the robot to actively recontact the body whenever physical contact between the ultrasound probe and the patient's body is lost; the human-guided mode renders the robot passive such that the doctor can safely intervene to manually reposition the probe. The integration of multiple modes allows the doctor to intervene safely at any time during the task and also maximizes the robot's autonomous scanning ability. The performance of the robot is validated on a collaborative scanning task of a carotid artery examination.

A Complementary Framework for Human-Robot Collaboration with a Mixed AR-Haptic Interface

There is invariably a trade-off between safety and efficiency for collaborative robots (cobots) in human-robot collaborations. Robots that interact minimally with humans can work with high speed and accuracy but cannot adapt to new tasks or respond to unforeseen changes, whereas robots that work closely with humans can but only by becoming passive to humans, meaning that their main tasks suspended and efficiency compromised. Accordingly, this paper proposes a new complementary framework for human-robot collaboration that balances the safety of humans and the efficiency of robots. In this framework, the robot carries out given tasks using a vision-based adaptive controller, and the human expert collaborates with the robot in the null space. Such a decoupling drives the robot to deal with existing issues in task space (e.g., uncalibrated camera, limited field of view) and in null space (e.g., joint limits) by itself while allowing the expert to adjust the configuration of the robot body to respond to unforeseen changes (e.g., sudden invasion, change of environment) without affecting the robot's main task. Additionally, the robot can simultaneously learn the expert's demonstration in task space and null space beforehand with dynamic movement primitives (DMP). Therefore, an expert's knowledge and a robot's capability are both explored and complementary. Human demonstration and involvement are enabled via a mixed interaction interface, i.e., augmented reality (AR) and haptic devices. The stability of the closed-loop system is rigorously proved with Lyapunov methods. Experimental results in various scenarios are presented to illustrate the performance of the proposed method.