Grasping and Assembling with Modular Robots

A wide variety of problems, from manufacturing to disaster response and space exploration, can benefit from robotic systems that can firmly grasp objects or assemble various structures, particularly in difficult, dangerous environments. In this thesis, we study the two problems, robotic grasping and assembly, with a modular robotic approach that can facilitate the problems with versatility and robustness. First, this thesis develops a theoretical framework for grasping objects with customized effectors that have curved contact surfaces, with applications to modular robots. We present a collection of grasps and cages that can effectively restrain the mobility of a wide range of objects including polyhedra. Each of the grasps or cages is formed by at most three effectors. A stable grasp is obtained by simple motion planning and control. Based on the theory, we create a robotic system comprised of a modular manipulator equipped with customized end-effectors and a software suite for planning and control of the manipulator. Second, this thesis presents efficient assembly planning algorithms for constructing planar target structures collectively with a collection of homogeneous mobile modular robots. The algorithms are provably correct and address arbitrary target structures that may include internal holes. The resultant assembly plan supports parallel assembly and guarantees easy accessibility in the sense that a robot does not have to pass through a narrow gap while approaching its target position. Finally, we extend the algorithms to address various symmetric patterns formed by a collection of congruent rectangles on the plane. The basic ideas in this thesis have broad applications to manufacturing (restraint), humanitarian missions (forming airfields on the high seas), and service robotics (grasping and manipulation)

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions

Dynamic modeling and simulation of a multi-fingered robot hand.

by Joseph Chun-kong Chan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 117-124).Abstract also in Chinese.Abstract --- p.iAcknowledgments --- p.ivList of Figures --- p.xiList of Tables --- p.xiiList of Algorithms --- p.xiiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Related Work --- p.5Chapter 1.3 --- Contributions --- p.7Chapter 1.4 --- Organization of the Thesis --- p.9Chapter 2 --- Contact Modeling: Kinematics --- p.11Chapter 2.1 --- Introduction --- p.11Chapter 2.2 --- Contact Kinematics between Two Rigid Bodies --- p.14Chapter 2.2.1 --- Contact Modes --- p.14Chapter 2.2.2 --- Montana's Contact Equations --- p.15Chapter 2.3 --- Finger Kinematics --- p.18Chapter 2.3.1 --- Finger Forward Kinematics --- p.19Chapter 2.3.2 --- Finger Jacobian --- p.21Chapter 2.4 --- Grasp Kinematics between a Finger and an Object --- p.21Chapter 2.4.1 --- Velocity Transformation between Different Coordinate Frames --- p.22Chapter 2.4.2 --- Grasp Kinematics for the zth Contact --- p.23Chapter 2.4.3 --- Different Fingertip Models and Different Contact Modes --- p.25Chapter 2.5 --- Velocity Constraints of the Entire System --- p.28Chapter 2.6 --- Summary --- p.29Chapter 3 --- Contact Modeling: Dynamics --- p.31Chapter 3.1 --- Introduction --- p.31Chapter 3.2 --- Multi-fingered Robot Hand Dynamics --- p.33Chapter 3.3 --- Object Dynamics --- p.35Chapter 3.4 --- Constrained System Dynamics --- p.37Chapter 3.5 --- Summary --- p.39Chapter 4 --- Collision Modeling --- p.40Chapter 4.1 --- Introduction --- p.40Chapter 4.2 --- Assumptions of Collision --- p.42Chapter 4.3 --- Collision Point Velocities --- p.43Chapter 4.3.1 --- Collision Point Velocity of the ith. Finger --- p.43Chapter 4.3.2 --- Collision Point Velocity of the Object --- p.46Chapter 4.3.3 --- Relative Collision Point Velocity --- p.47Chapter 4.4 --- Equations of Collision --- p.47Chapter 4.4.1 --- Sliding Mode Collision --- p.48Chapter 4.4.2 --- Sticking Mode Collision --- p.49Chapter 4.5 --- Summary --- p.51Chapter 5 --- Dynamic Simulation --- p.53Chapter 5.1 --- Introduction --- p.53Chapter 5.2 --- Architecture of the Dynamic Simulation System --- p.54Chapter 5.2.1 --- Input Devices --- p.54Chapter 5.2.2 --- Dynamic Simulator --- p.58Chapter 5.2.3 --- Virtual Environment --- p.60Chapter 5.3 --- Methodologies and Program Flow of the Dynamic Simulator --- p.60Chapter 5.3.1 --- Interference Detection --- p.61Chapter 5.3.2 --- Constraint-based Simulation --- p.63Chapter 5.3.3 --- Impulse-based Simulation --- p.66Chapter 5.4 --- Summary --- p.69Chapter 6 --- Simulation Results --- p.71Chapter 6.1 --- Introduction --- p.71Chapter 6.2 --- Change of Grasping Configurations --- p.71Chapter 6.3 --- Rolling Contact --- p.76Chapter 6.4 --- Sliding Contact --- p.76Chapter 6.5 --- Collisions --- p.85Chapter 6.6 --- Dextrous Manipulation Motions --- p.93Chapter 6.7 --- Summary --- p.94Chapter 7 --- Conclusions --- p.99Chapter 7.1 --- Summary of Contributions --- p.99Chapter 7.2 --- Future Work --- p.100Chapter 7.2.1 --- Improvement of Current System --- p.100Chapter 7.2.2 --- Applications --- p.101Chapter A --- Montana's Contact Equations for Finger-object Contact --- p.103Chapter A.1 --- Local Coordinates Charts --- p.103Chapter A.2 --- "Curvature, Torsion and Metric Tensors" --- p.104Chapter A.3 --- Montana's Contact Equations --- p.106Chapter B --- Finger Dynamics --- p.108Chapter B.1 --- Forward Kinematics of a Robot Finger --- p.108Chapter B.1.1 --- Link-coordinate Transformation --- p.109Chapter B.1.2 --- Forward Kinematics --- p.109Chapter B.2 --- Dynamic Equation of a Robot Finger --- p.110Chapter B.2.1 --- Kinetic and Potential Energy --- p.110Chapter B.2.2 --- Lagrange's Equation --- p.111Chapter C --- Simulation Configurations --- p.113Chapter C.1 --- Geometric models --- p.113Chapter C.2 --- Physical Parameters --- p.113Chapter C.3 --- Simulation Parameters --- p.116Bibliography --- p.12

ReHand - a portable assistive rehabilitation hand exoskeleton

This dissertation presents a synthesis of a novel underactuated exoskeleton (namely ReHand2) thought and designed for a task-oriented rehabilitation and/or for empower the human hand. The first part of this dissertation shows the current context about the robotic rehabilitation with a focus on hand pathologies, which influence the hand capability. The chapter is concluded with the presentation of ReHand2. The second chapter describes the human hand biomechanics. Starting from the definition of human hand anatomy, passing through anthropometric data, to taxonomy on hand grasps and finger constraints, both from static and dynamic point of view. In addition, some information about the hand capability are given. The third chapter analyze the current state of the art in hand exoskeleton for rehabilitation and empower tasks. In particular, the chapter presents exoskeleton technologies, from mechanisms to sensors, passing though transmission and actuators. Finally, the current state of the art in terms of prototype and commercial products is presented. The fourth chapter introduces the concepts of underactuation with the basic explanation and the classical notation used typically in the prosthetic field. In addition, the chapter describe also the most used differential elements in the prosthetic, follow by a statical analysis. Moreover typical transmission tree at inter-finger level as well as the intra- finger underactuation are explained . The fifth chapter presents the prototype called ReHand summarizing the device description and explanation of the working principle. It describes also the kinetostatic analysis for both, inter- and the intra-finger modules. in the last section preliminary results obtained with the exoskeleton are shown and discussed, attention is pointed out on prototype’s problems that have carry out at the second version of the device. The sixth chapter describes the evolution of ReHand, describing the kinematics and dynamics behaviors. In particular, for the mathematical description is introduced the notation used in order to analyze and optimize the geometry of the entire device. The introduced model is also implemented in Matlab Simulink environment. Finally, the chapter presents the new features. The seventh chapter describes the test bench and the methodologies used to evaluate the device statical, and dynamical performances. The chapter presents and discuss the experimental results and compare them with simulated one. Finally in the last chapter the conclusion about the ReHand project are proposed as well as the future development. In particular, the idea to test de device in relevant environments. In addition some preliminary considerations about the thumb and the wrist are introduced, exploiting the possibility to modify the entire layout of the device, for instance changing the actuator location

Intent-Recognition-Based Traded Control for Telerobotic Assembly over High-Latency Telemetry

As we deploy robotic manipulation systems into unstructured real-world environments, the tasks which those robots are expected to perform grow very quickly in complexity. These tasks require a greater number of possible actions, more variable environmental conditions, and larger varieties of objects and materials which need to be manipulated. This in turn leads to a greater number of ways in which elements of a task can fail. When the cost of task failure is high, such as in the case of surgery or on-orbit robotic interventions, effective and efficient task recovery is essential. Despite ever-advancing capabilities, however, the current and near future state-of-the-art in fully autonomous robotic manipulation is still insufficient for many tasks in these critical applications. Thus, successful application of robotic manipulation in many application domains still necessitates a human operator to directly teleoperate the robots over some communications infrastructure. However, any such infrastructure always incurs some unavoidable round-trip telemetry latency depending on the distances involved and the type of remote environment. While direct teleoperation is appropriate when a human operator is physically close to the robots being controlled, there are still many applications in which such proximity is infeasible. In applications which require a robot to be far from its human operator, this latency can approach the speed of the relevant task dynamics, and performing the task with direct telemanipulation can become increasingly difficult, if not impossible. For example, round-trip delays for ground-controlled on-orbit robotic manipulation can reach multiple seconds depending on the infrastructure used and the location of the remote robot. The goal of this thesis is to advance the state-of-the art in semi-autonomous telemanipulation under multi-second round-trip communications latency between a human operator and remote robot in order to enable more telerobotic applications. We propose a new intent-recognition-based traded control (IRTC) approach which automatically infers operator intent and executes task elements which the human operator would otherwise be unable to perform. What makes our approach more powerful than the current approaches is that we prioritize preserving the operator's direct manual interaction with the remote environment while only trading control over to an autonomous subsystem when the operator-local intent recognition system automatically determines what the operator is trying to accomplish. This enables operators to perform unstructured and a priori unplanned actions in order to quickly recover from critical task failures. Furthermore, this thesis also describes a methodology for introducing and improving semi-autonomous control in critical applications. Specifically, this thesis reports (1) the demonstration of a prototype system for IRTC-based grasp assistance in the context of transatlantic telemetry delays, (2) the development of a systems framework for IRTC in semi-autonomous telemanipulation, and (3) an evaluation of the usability and efficacy of that framework with an increasingly complex assembly task. The results from our human subjects experiments show that, when incorporated with sufficient lower-level capabilities, IRTC is a promising approach to extend the reach and capabilities of on-orbit telerobotics and future in-space operations

Grasp planning for object manipulation by an autonomous robot

L'évolution autonome d'un robot dans un environnement évolutif nécessite qu'il soit doté de capacités de perception, d'action et de décision suffisantes pour réaliser la tâche assignée. Une tâche essentielle en robotique est la manipulation d'objets et d'outils. Elle intervient non seulement pour un robot seul mais également dans des situations d'interaction avec un humain ou un autre robot quand il s’agit d’échanger des objets ou de les manipuler conjointement.\ud Cette thèse porte sur la planification de tâches de manipulation d'objets pour un robot autonome dans un environnement humain. Une architecture logicielle susceptible de résoudre ce type de problèmes au niveau géométrique est proposée. Généralement, une tâche de manipulation commence par une opération de saisie dont la qualité conditionne fortement la réussite de la tâche et pour laquelle nous proposons un planificateur basé sur les propriétés inertielles de l'objet et une décomposition en éléments quasi-convexes tout en prenant en compte les contraintes imposées par le système mobile complet dans un environnement donné.\ud Les résultats sont validés en simulation et sur le robot sur la base d’une extension des outils de planification développés au LAAS-CNRS. Le modèle géométrique 3D de l’objet peut être connu a priori ou bien acquis en ligne. Des expérimentations menées sur un robot manipulateur mobile équipé d'une pince à trois points de contacts, de capteurs de force et d'une paire de caméras stéréoscopiques ont montré la validité de l'approche.\ud The autonomous robot performance in a dynamic environment requires advanced perception, action and decision capabilities. Interaction with the environment plays a key role for a robot and it is well illustrated in object and/or tool manipulation. Interaction with humans or others robots can consist in object exchanges.\ud This thesis deals with object manipulation planning by an autonomous robot in human environments. A software architecture is proposed that is capable to solve such problems at the geometrical level. In general, a manipulation task starts by a grasp operation which quality influences strongly the success of the overall task. We propose a planner based on object inertial properties and an approximate convex decomposition. The whole mobile system taken into account in the planning process.\ud The planner has been completely implemented as an extension of the planning tools developed at LAAS-CNRS. Its results have been tested in simulation and on a robotic platform. Object models may be known a priori or acquired on-line. Experiments have been carried out with a mobile manipulator equipped with a three fingers gripper, a wrist force sensor and a stereo camera system in order to validate the approach.\ud \ud \u

Motion Planning for Manipulation With Heuristic Search

Heuristic searches such as A* search are a popular means of finding least-cost plans due to their generality, strong theoretical guarantees on completeness and optimality, simplicity in implementation, and consistent behavior. In planning for robotic manipulation, however, these techniques are commonly thought of as impractical due to the high-dimensionality of the planning problem. As part of this thesis work, we have developed a heuristic search-based approach to motion planning for manipulation that does deal effectively with the high-dimensionality of the problem. In this thesis, I will present the approach together with its theoretical properties and show how to apply it to single-arm and dual-arm motion planning with upright constraints on a PR2 robot operating in non-trivial cluttered spaces. Then I will explain how we extended our approach to manipulation planning for n-arms with regrasping. In this work, the planner itself makes all of the discrete decisions, including which arm to use for the pickup and putdown, whether handoffs are necessary and how the object should be grasped at each step along the way. An extensive experimental analysis in both simulation and on a physical PR2 shows that, in terms of runtime, our approach is on par with some of the most common sampling-based approaches. This includes benchmarking our planning framework on two domains that we constructed that are common to manufacturing: pick-and-place of fast moving objects and the autonomous assembly of small objects. Between these applications, the planner exhibited fast planning times and the ability to robustly plan paths into and out of tight working environments that are common to assembly. The closing work of this thesis includes an exhaustive study of the natural tradeoff that occurs between planning efficiency versus solution quality for different values of the heuristic inflation factor. A comparison of the solution quality of our planner to paths computed by an asymptotically optimal approach given a great deal of time for path optimization is included as well. Finally, a set of experimental results are included that show that due to our approach\u27s deterministic cost-minimization, similar input tends to lead to similarity in the output. This kind of local consistency is important to the predictability of the robot\u27s motions and contributes to human-robot safety