Remote-controlled vascular interventional robots have great potential for use in minimally invasive vascular surgeries in recent years due to their ability to reduce the occupational risk of surgeons and improve the stability and accuracy of surgical procedures. However, blood vessels will suffer from the damage caused by collision with medical instruments to some extent even though the surgeries are very successful. Moreover, when surgeons perform unsafe operations, the unsafe operations will not only seriously affect surgical safety (or even cause serious complications) but also restrict the continuity of operation. In this article, a multilevel concept for operating force is first introduced into surgical procedures as a reference for the choice and design of operation strategies. Based on this concept, a novel multilevel operation strategy is first proposed to reduce blood vessel damage, ensure surgical safety, and allow for continuous operation. This strategy can remind surgeons about the operative conditions in real-time, reduce collision to blood vessels, and eliminate unsafe operations online. A prototype was fabricated and calibrated through calibration experiments and the performance of the multilevel operation strategy was validated through in vitro and ex vivo experiments. Experimental results demonstrate the engineering effectiveness of the proposed method and motivate the need for further in vivo studies to evaluate improvement on surgical safety.

Aiming to deal with underwater localization for small-size robots in GPS-denied and structured environment, this paper proposed a novel multi-sensor fusion-based self-localization system using low-cost sensors. Based on multi-sensor information fusion, an Extended Kalman Filter (EKF) is utilized to synthesize the multi-source information from an Inertial Measurement Unit (IMU), optical flow, pressure sensor and ArUco markers, which enables the robot obtain a highly precise positioning. This method also can reduce the location drift over time owing to the loss of markers in pure markers-based localization. Specially, a velocity correction model is proposed using the angle information obtained by IMU, which can compensate optical flow-based velocity estimation errors caused by robot posture changes. Finally, to validate the performance of the proposed self-localization system, simulations are conducted using Gazebo simulator on the robot operating system (ROS). Moreover, a series of experiments in an indoor swimming pool are presented. Results of the proposed method and dead reckoning are compared in simulation and experiment to demonstrate the robustness and feasibility of the proposed localization system.