Mapping haptic exploratory procedures to multiple shape representations

Research in human haptics has revealed a number of exploratory procedures (EPs) that are used in determining attributes on an object, particularly shape. This research has been used as a paradigm for building an intelligent robotic system that can perform shape recognition from touch sensing. In particular, a number of mappings between EPs and shape modeling primitives have been found. The choice of shape primitive for each EP is discussed, and results from experiments with a Utah-MIT dextrous hand system are presented. A vision algorithm to complement active touch sensing for the task of autonomous shape recovery is also presented

Pose-Based Tactile Servoing: Controlled Soft Touch using Deep Learning

This article describes a new way of controlling robots using soft tactile sensors: pose-based tactile servo (PBTS) control. The basic idea is to embed a tactile perception model for estimating the sensor pose within a servo control loop that is applied to local object features such as edges and surfaces. PBTS control is implemented with a soft curved optical tactile sensor (the BRL TacTip) using a convolutional neural network trained to be insensitive to shear. In consequence, robust and accurate controlled motion over various complex 3D objects is attained. First, we review tactile servoing and its relation to visual servoing, before formalising PBTS control. Then, we assess tactile servoing over a range of regular and irregular objects. Finally, we reflect on the relation to visual servo control and discuss how controlled soft touch gives a route towards human-like dexterity in robots.Comment: A summary video is available here https://youtu.be/12-DJeRcfn0 *NL and JL contributed equally to this wor

Active haptic exploration for 3D shape reconstruction.

by Fung Wai Keung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 146-151).Acknowledgements --- p.viiiAbstract --- p.1Chapter 1 --- Overview --- p.3Chapter 1.1 --- Tactile Sensing in Human and Robot --- p.4Chapter 1.1.1 --- Human Hands and Robotic Hands --- p.4Chapter 1.1.2 --- Mechanoreceptors in skin and Tactile Sensor Arrays --- p.7Chapter 1.2 --- Motivation --- p.12Chapter 1.3 --- Objectives --- p.13Chapter 1.4 --- Related Work --- p.14Chapter 1.4.1 --- Using Vision Alone --- p.15Chapter 1.4.2 --- Integration of Vision and Touch --- p.15Chapter 1.4.3 --- Using Touch Sensing Alone --- p.17Chapter 1.4.3.1 --- Ronald S. Fearing's Work --- p.18Chapter 1.4.3.2 --- Peter K. Allen's Work --- p.22Chapter 1.5 --- Outline --- p.26Chapter 2 --- Geometric Models --- p.27Chapter 2.1 --- Introduction --- p.27Chapter 2.2 --- Superquadrics --- p.27Chapter 2.2.1 --- 2D Superquadrics --- p.27Chapter 2.2.2 --- 3D Superquadrics --- p.29Chapter 2.3 --- Model Recovery of Superquadric Models --- p.31Chapter 2.3.1 --- Problem Formulation --- p.31Chapter 2.3.2 --- Least Squares Optimization --- p.33Chapter 2.4 --- Free-Form Deformations --- p.34Chapter 2.4.1 --- Bernstein Basis --- p.36Chapter 2.4.2 --- B-Spline Basis --- p.38Chapter 2.5 --- Other Geometric Models --- p.41Chapter 2.5.1 --- Generalized Cylinders --- p.41Chapter 2.5.2 --- Hyperquadrics --- p.42Chapter 2.5.3 --- Polyhedral Models --- p.44Chapter 2.5.4 --- Function Representation --- p.45Chapter 3 --- Sensing Strategy --- p.54Chapter 3.1 --- Introduction --- p.54Chapter 3.2 --- Sensing Algorithm --- p.55Chapter 3.2.1 --- Assumption of objects --- p.55Chapter 3.2.2 --- Haptic Exploration Procedures --- p.56Chapter 3.3 --- Contour Tracing --- p.58Chapter 3.4 --- Tactile Sensor Data Preprocessing --- p.59Chapter 3.4.1 --- Data Transformation and Sensor Calibration --- p.60Chapter 3.4.2 --- Noise Filtering --- p.61Chapter 3.5 --- Curvature Determination --- p.64Chapter 3.6 --- Step Size Determination --- p.73Chapter 4 --- 3D Shape Reconstruction --- p.80Chapter 4.1 --- Introduction --- p.80Chapter 4.2 --- Correspondence Problem --- p.81Chapter 4.2.1 --- Affine Invariance Property of B-splines --- p.84Chapter 4.2.2 --- Point Inversion Problem --- p.87Chapter 4.3 --- Parameter Triple Interpolation --- p.91Chapter 4.4 --- 3D Object Shape Reconstruction --- p.94Chapter 4.4.1 --- Heuristic Approach --- p.94Chapter 4.4.2 --- Closed Contour Recovery --- p.97Chapter 4.4.3 --- Control Lattice Recovery --- p.102Chapter 5 --- Implementation --- p.105Chapter 5.1 --- Introduction --- p.105Chapter 5.2 --- Implementation Tool - MATLAB --- p.105Chapter 5.2.1 --- Optimization Toolbox --- p.107Chapter 5.2.2 --- Splines Toolbox --- p.108Chapter 5.3 --- Geometric Model Implementation --- p.109Chapter 5.3.1 --- FFD Examples --- p.111Chapter 5.4 --- Shape Reconstruction Implementation --- p.112Chapter 5.5 --- 3D Model Reconstruction Examples --- p.120Chapter 5.5.1 --- Example 1 --- p.120Chapter 5.5.2 --- Example 2 --- p.121Chapter 6 --- Conclusion --- p.128Chapter 6.1 --- Future Work --- p.129Appendix --- p.133Bibliography --- p.14

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