The use of robots in health care has increased dramatically over the last decade. One area of research has been to use robots to conduct ultrasound examinations, either controlled by a physician or autonomously. This paper examines the possibility of using the commercial robot UR5 from Universal Robots to make a tele-operated robotic ultrasound system. Physicians diagnosing patients using ultrasound probes are prone to repetitive strain injuries, as they are required to hold the probe in uncomfortable positions and exert significant static force. The main application for the system is to relieve the physician of this strain by letting the them control a robot that holds the probe. A set of requirements for the system is derived from the state-of-the-art systems found in the research literature. The system is developed through a low-level interface for the robot, effectively building a new software framework for controlling it. Compliance force control and forward flow haptic control of the robot was implemented. Experiments are conducted to quantify the performance of the two control schemes. The force control is estimated to have a bandwidth of 16.6 Hz, while the haptic control is estimated to have a bandwidth of 65.4 Hz for the position control of the slave and 13.4 Hz for the force control of the master. Overall, the system meets the derived requirements and the main conclusion is that it is feasible to use the UR5 robot for robotic ultrasound applications.
The introduction of robotic surgery within the operating rooms has significantly improved the quality of many surgical procedures. Recently, the research on medical robotic systems focused on increasing the level of autonomy in order to give them the possibility to carry out simple surgical actions autonomously. This paper reports on the development of technologies for introducing automation within the surgical workflow. The results have been obtained during the ongoing FP7 European funded project Intelligent Surgical Robotics (I-SUR). The main goal of the project is to demonstrate that autonomous robotic surgical systems can carry out simple surgical tasks effectively and without major intervention by surgeons. To fulfil this goal, we have developed innovative solutions (both in terms of technologies and algorithms) for the following aspects: fabrication of soft organ models starting from CT images, surgical planning and execution of movement of robot arms in contact with a deformable environment, designing a surgical interface minimizing the cognitive load of the surgeon supervising the actions, intra-operative sensing and reasoning to detect normal transitions and unexpected events. All these technologies have been integrated using a component-based software architecture to control a novel robot designed to perform the surgical actions under study. In this work we provide an overview of our system and report on preliminary results of the automatic execution of needle insertion for the cryoablation of kidney tumours.
This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator. Then the resulting architecture is used to develop distributed prediction algorithms using recurrent neural networks. The proposed RNN-based parallel predictive scheme does not rely on computationally intensive algorithms; therefore, it is useful in real-time applications. Furthermore, simulations are shown to illustrate the approach performance on soft continuum elastica, and the approach is also validated through an experiment on a magnetically-actuated soft continuum manipulator. The results demonstrate that the presented model can outperform classical modeling approaches such as the Cosserat rod model while also shows possibilities for being used in practice.
In this paper, a new method of path planning for unmanned ground vehicles (UGVs) on terrain is developed. For UGVs moving on terrain, path traversability and collision avoidance are important factors. If traversability is not considered, the planned path may lead a UGV into areas that will cause rough vehicle motion or lead to the UGV getting stuck if the traversability is low. The proposed path planning method is based on the Hybrid A* algorithm and uses estimated terrain traversability to find the path that optimizes both traversability and distance for the UGV.The path planning method is demonstrated using simulated traversability maps and is compared to the original Hybrid A* algorithm. The method is also verified through real-time experiments in real terrain, further demonstrating the benefits of terrain traversability optimization using the proposed path planning method. In the experiments, the proposed method was successfully applied for autonomous driving over distances of up to 270 m in rough terrain. Compared with the existing Hybrid A* method, the proposed method produces more traversable paths.
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