Rigid robotic manipulators employ traditional sensors such as encoders or potentiometers to measure joint angles and determine end-effector position. Manipulators that are flexible, however, introduce motions that are much more difficult to measure. This is especially true for continuum manipulators that articulate by means of material compliance. In this paper, we present a vision based system for quantifying the 3-D shape of a flexible manipulator in real-time. The sensor system is validated for accuracy with known point measurements and for precision by estimating a known 3-D shape. We present two applications of the validated system relating to the open-loop control of a tendon driven continuum manipulator. In the first application, we present a new continuum manipulator model and use the sensor to quantify 3-D performance. In the second application, we use the shape sensor system for model parameter estimation in the absence of tendon tension information.

Traditional image mosaicing usually relies on rigid image transformations. In many medical applications, however, tissue deformation during image acquisition or 3D parallax effects may require nonrigid transformations in the mosaicing process. This paper presents a new method that integrates deformable surface models into the image mosaicing algorithms. Our approach has two main contributions. First, we present a global alignment algorithm to efficiently deal with accumulated image registration errors. Second, we introduce a local alignment algorithm to accommodate local scene deformations. These two problems are integrated into a single optimization problem that simultaneously recovers the motion of the camera as well as the structure of the scene. Our approach is demonstrated on simulations, images from a hand-held digital camera, and microscopic images acquired with a micro-endoscope.

This paper presents the design of an optical fiber proximity sensor for haptic exploration with a robotic finger. The sensor uses emitter and receiver optical fiber pairs to measure the intensity of light reflected off surrounding objects in a 2-D workspace. We present the design and construction a 32-point sensor array mounted within a 36 mm diameter finger and describe software techniques to process data acquired by an inexpensive web-cam. We experimentally characterize the sensor performance and demonstrate applications for haptic exploration such as pre-contact velocity reduction and non-contact contour following based on object curvature.