We present a novel method for enforcing nonlinear inequality constraints in the estimation of a high degree of freedom robotic system within a Kalman filter. Our constrained Kalman filtering technique is based on a new concept, which we call uncertainty projection, that projects the portion of the uncertainty ellipsoid that does not satisfy the constraint onto the constraint surface. A new PDF is then generated with an efficient update procedure that is guaranteed to reduce the uncertainty of the system. The application we have targeted for this work is the localization and automatic registration of a robotic surgical probe relative to preoperative images during image-guided surgery. We demonstrate the feasibility of our constrained filtering approach with data collected from an experiment involving a surgical robot navigating on the epicardial surface of a porcine heart.
Human-Robot Physical Interaction is an important attribute for robots operating in human environments. The authors illustrate some basic physically interactive behaviors with dynamically stable mobile robots using the ballbot as an example. The ballbot is a dynamically stable mobile robot moving on a single spherical wheel. The dynamic stability and robust controllers enable the ballbot to be physically moved with ease. The authors also demonstrate other behaviors like human intent detection and learn-repeat behavior on the real robot.
In this paper, we present a filtering method for estimating the shape and end effector pose of a highly articulated surgical snake robot. Our algorithm introduces new kinematic models that are used in the prediction step of an extended Kalman filter whose update step incorporates measurements from a 5-DOF electromagnetic tracking sensor situated at the distal end of the robot. A single tracking sensor is sufficient for estimating the shape of the system because the robot is inherently a follow-the-leader mechanism with well defined motion characteristics. We therefore show that, with appropriate steering motion, the state of the filter is fully observable. The goal of our shape estimation algorithm is to create a more accurate and representative 3D rendered visualization for image-guided surgery. We demonstrate the feasibility of our method with results from an animal experiment in which our shape and pose estimate was used as feedback in a control scheme that semi-autonomously drove the robot along the epicardial surface of a porcine heart.
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