The Raven-II is a platform for collaborative research on advances in surgical robotics. Seven universities have begun research using this platform. The Raven-II system has two 3-DOF spherical positioning mechanisms capable of attaching interchangeable four DOF instruments. The Raven-II software is based on open standards such as Linux and ROS to maximally facilitate software development. The mechanism is robust enough for repeated experiments and animal surgery experiments, but is not engineered to sufficient safety standards for human use. Mechanisms in place for interaction among the user community and dissemination of results include an electronic forum, an online software SVN repository, and meetings and workshops at major robotics conferences.
Forbidden region virtual fixtures protect objects from unwanted contact with a robot. In this paper, we propose a method for creating forbidden region haptic virtual fixtures for teleoperation from streaming point clouds obtained by an RGB-D camera. Upon violating the protected area, the operator receives force feedback that opposes motion inside the forbidden region. Three architectures for creating virtual fixtures are presented and their advantages and disadvantages are described. The proposed methods have the ability to implement constraints and can handle dynamic environments in real-time. The effectiveness of the methods is demonstrated in experiments with a surgical robot.
This paper proposes a novel algorithm for haptic rendering from time varying point clouds captured using an Xbox Kinect RGB-D camera. Existing methods for point-based haptic rendering using a proxy can not directly be applied in this situation since no information about the underlying objects is given. This paper extends the notion of proxy to point clouds. The haptic algorithm presented here renders haptic forces from point clouds captured in real-time representing both static and dynamic objects.
This paper proposes a method to estimate and compensate for the changes of cable tension in the control of cable driven mechanisms. Cable tension may depend on various factors, including mechanism design, fabrication and operation. In many systems it is also an adjustable parameter that affects the performance of the control system. An implementation of the unscented Kalman filter is used for the simultaneous estimation of the states and parameters of a cable driven mechanism. Changes in cable tension are captured in the estimated parameters which, along with system states, are used by a model predictive controller to generate appropriate control actions. The method is described and its effectiveness is shown for a single degree of freedom cable driven robot. In addition, the correlation between the cable tension and the estimated robot parameters provides a way of estimating the tension. It is shown that cable tension can be inferred from one of the estimated robot parameters, namely cable stiffness.
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