The problem of image based visual servoing for robots working in a cluttered dynamic environment is addressed in this paper. It is assumed that the environment is observed by depth sensors which allow to measure the distance between any moving obstacle and the robot. Also an eye-in-hand camera is used to extract image features. The main idea is to control suitable image moments and to relax a certain number of robot's degrees of freedom during the interaction phase. If an obstacle approaches the robot, the main visual servoing task is relaxed partially or completely, while the image features are kept in the camera field of view by controlling the image moments. Fuzzy rules are used to set the desired values of the image moments. Beside that, the relaxed redundancy of the robot is exploited to avoid collisions. After removing the risk of collision, the main visual servoing task is resumed. The effectiveness of the algorithm is shown by several case studies on a KUKA LWR 4 robot arm
In this paper, the application of a robotic arm for needle-based medical interventions under a preoperative CT-scan is investigated. The proposed setup consists of a robot manipulator with a customized needle guide mounted on its end-effector. A workflow that considers all the issues related to the registration and needle positioning algorithms is proposed. The so-called constrained admittance control approach is proposed to provide semi-autonomous positioning of the needle guide on the target path based on CT-scan images. The proposed algorithms are verified experimentally through phantom studies.
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