Tactile mapping of harsh, constrained environments, with an application to oil wells

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. [110]-114).This work develops a practical approach to explore rough environments when time is critical. The harsh environmental conditions prevent the use of range, force/torque or tactile sensors. A representative case is the mapping of oil wells. In these conditions, tactile exploration is appealing. In this work, the environment is mapped tactilely, by a manipulator whose only sensors are joint encoders. The robot autonomously explores the environment collecting few, sparse tactile data and monitoring its free movements. These data are used to create a model of the surface in real time and to choose the robot's movements to reduce the mapping time. First, the approach is described and its feasibility demonstrated. Real-time impedance control allows a robust robot movement and the detection of the surface using a manipulator mounting only position sensors. A representation based on geometric primitives describes the surface using the few, sparse data available. The robustness of the method is tested against surface roughness and different surrounding fluids. Joint backlash strongly affect the robot's precision, and it is inevitable because of the thermal expansion in the joints. Here, a new strategy is developed to compensate for backlash positioning errors, by simultaneously identifying the surface and the backlash values. Second, an exploration strategy to map a constraining environment with a manipulator is developed. To maximize the use of the acquired data, this work proposes a hybrid approach involving both workspace and configuration space. The amount of knowledge of the environment is evaluated with an approach based on information theory, and the robot's movements are chosen to maximize the expected increase of such knowledge. Since the robot only possesses position sensors, the location along the robot where contact with the surface occurs cannot be determined with certainty. Thus a new approach is developed, that evaluates the probability of contact with specific parts of the robot and classifies and uses the data according to the different types of contact. This work is validated with simulations and experiments with a prototype manipulator specifically designed for this application.by Francesco Mazzini.Ph.D

Sensitive Skin for Robotics

This thesis explores two novel ways of reducing the data complexity of tactile sensing. The thesis begins by examining the state-of-the art in tactile sensing, not only examining the sensor construction and interpretation of data but also the motivation for these designs. The thesis then proposes two methods for reducing the complexity of data in tactile sensing. The first is a low-power tactile sensing array exploiting a novel application of a pressure-sensitive material called quantum tunnelling composite. The properties of this material in this array form are shown to be beneficial in robotics. The electrical characteristics of the material are also explored. A bit-based structure for representing tactile data called Bitworld is then defined and its computational performance is characterised. It is shown that this bit-based structure outperforms floating-point arrays by orders of magnitude. This structure is then shown to allow high-resolution images to be produced by combining low resolution sensor arrays with equivalent functional performance to a floating-point array, but with the advantages of computational efficiency. Finally, an investigation into making Bitworld robust in the presence of positional noise is described with simulations to verify that such robustness can be achieved. Overall, the sensor and data structure described in this thesis allow simple, but effective tactile systems to be deployed in robotics without requiring a significant commitment of computational or power resources on the part of a robot designer.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Probabilistic Feature-Based Registration for Interventional Medicine

The need to compute accurate spatial alignment between multiple representations of patient anatomy is a problem that is fundamental to many applications in computer-integrated interventional medicine. One class of methods for computing such alignments is feature-based registration, which aligns geometric information of the shapes being registered, such as salient landmarks or models of shape surfaces. A popular algorithm for surface-based registration is the Iterative Closest Point (ICP) algorithm, which treats one shape as a cloud of points that is registered to a second shape by iterating between point-correspondence and point-registration phases until convergence. In this dissertation, a class of "most likely point" variants on the ICP algorithm is developed that offers several advantages over ICP, such as high registration accuracy and the ability to confidently assess the quality of a registration outcome. The proposed algorithms are based on a probabilistic interpretation of the registration problem, wherein the point-correspondence and point-registration phases optimize the probability of shape alignment based on feature uncertainty models rather than minimizing the Euclidean distance between the shapes as in ICP. This probabilistic framework is used to model anisotropic errors in the shape measurements and to provide a natural context for incorporating oriented-point data into the registration problem, such as shape surface normals. The proposed algorithms are evaluated through a range of simulation-, phantom-, and clinical-based studies, which demonstrate significant improvement in registration outcomes relative to ICP and state-of-the-art methods