Multifingered grasping for robotic manipulation

Robotic hand increases the adaptability of grasping and manipulating objects with its system.But this added adaptability of grasping convolute the process of grasping the object. The analysis of the grasp is very much complicated and large number of configuration for grasping is to be investigated. Handling of objects with irregular shapes and that of flexible/soft objects by ordinary robot grippers is difficult. It is required that various objects with different shapes or sizes could be grasped and manipulated by one robot hand mechanism for the sake of factory automation and labour saving. Dexterous grippers will be the appropriate solution to such problems. Corresponding to such needs, the present work is towards the design and development of an articulated mechanical hand with five fingers and twenty five degrees-of-freedom having an improved grasp capability. In the work, the distance between the Thumb and Finger and the workspace generated by the hand is calculated so as to know about the size and shape of the object that could be grasped.Further the Force applied by the Fingers and there point of application is also being calculated so as to have a stable force closure grasp. The method introduced in present study reduces the complexity and computational burden of grasp synthesis by examining grasps at the finger level. A detailed study on the force closure grasping capability and quality has been carried out. The workspace of the five fingered hand has been used as the maximum spatial envelope. The problem has been considered with positive grips constructed as non-negative linear combinations of primitive and pure wrenches. The attention has been restricted to systems of wrenches generated by the hand fingers assuming Coulomb friction. In order to validate the algorithm vis-a-vis the designed five fingered dexterous hand, example problems have been solved with multiple sets of contact points on various shaped objects.Since the designed hand is capable of enveloping and grasping an object mechanically, it can be used conveniently and widely in manufacturing automation and for medical rehabilitation purpose. This work presents the kinematic design and the grasping analysis of such a hand

Whole-Hand Robotic Manipulation with Rolling, Sliding, and Caging

Traditional manipulation planning and modeling relies on strong assumptions about contact. Specifically, it is common to assume that contacts are fixed and do not slide. This assumption ensures that objects are stably grasped during every step of the manipulation, to avoid ejection. However, this assumption limits achievable manipulation to the feasible motion of the closed-loop kinematic chains formed by the object and fingers. To improve manipulation capability, it has been shown that relaxing contact constraints and allowing sliding can enhance dexterity. But in order to safely manipulate with shifting contacts, other safeguards must be used to protect against ejection. “Caging manipulation,” in which the object is geometrically trapped by the fingers, can be employed to guarantee that an object never leaves the hand, regardless of constantly changing contact conditions. Mechanical compliance and underactuated joint coupling, or carefully chosen design parameters, can be used to passively create a caging grasp – protecting against accidental ejection – while simultaneously manipulating with all parts of the hand. And with passive ejection avoidance, hand control schemes can be made very simple, while still accomplishing manipulation. In place of complex control, better design can be used to improve manipulation capability—by making smart choices about parameters such as phalanx length, joint stiffness, joint coupling schemes, finger frictional properties, and actuator mode of operation. I will present an approach for modeling fully actuated and underactuated whole-hand-manipulation with shifting contacts, show results demonstrating the relationship between design parameters and manipulation metrics, and show how this can produce highly dexterous manipulators

Grasp plannind under task-specific contact constraints

Several aspects have to be addressed before realizing the dream of a robotic hand-arm system with human-like capabilities, ranging from the consolidation of a proper mechatronic design, to the development of precise, lightweight sensors and actuators, to the efficient planning and control of the articular forces and motions required for interaction with the environment. This thesis provides solution algorithms for a main problem within the latter aspect, known as the {\em grasp planning} problem: Given a robotic system formed by a multifinger hand attached to an arm, and an object to be grasped, both with a known geometry and location in 3-space, determine how the hand-arm system should be moved without colliding with itself or with the environment, in order to firmly grasp the object in a suitable way. Central to our algorithms is the explicit consideration of a given set of hand-object contact constraints to be satisfied in the final grasp configuration, imposed by the particular manipulation task to be performed with the object. This is a distinguishing feature from other grasp planning algorithms given in the literature, where a means of ensuring precise hand-object contact locations in the resulting grasp is usually not provided. These conventional algorithms are fast, and nicely suited for planning grasps for pick-an-place operations with the object, but not for planning grasps required for a specific manipulation of the object, like those necessary for holding a pen, a pair of scissors, or a jeweler's screwdriver, for instance, when writing, cutting a paper, or turning a screw, respectively. To be able to generate such highly-selective grasps, we assume that a number of surface regions on the hand are to be placed in contact with a number of corresponding regions on the object, and enforce the fulfilment of such constraints on the obtained solutions from the very beginning, in addition to the usual constraints of grasp restrainability, manipulability and collision avoidance. The proposed algorithms can be applied to robotic hands of arbitrary structure, possibly considering compliance in the joints and the contacts if desired, and they can accommodate general patch-patch contact constraints, instead of more restrictive contact types occasionally considered in the literature. It is worth noting, also, that while common force-closure or manipulability indices are used to asses the quality of grasps, no particular assumption is made on the mathematical properties of the quality index to be used, so that any quality criterion can be accommodated in principle. The algorithms have been tested and validated on numerous situations involving real mechanical hands and typical objects, and find applications in classical or emerging contexts like service robotics, telemedicine, space exploration, prosthetics, manipulation in hazardous environments, or human-robot interaction in general

Grasp Stability Analysis with Passive Reactions

Despite decades of research robotic manipulation systems outside of highly-structured industrial applications are still far from ubiquitous. Perhaps particularly curious is the fact that there appears to be a large divide between the theoretical grasp modeling literature and the practical manipulation community. Specifically, it appears that the most successful approaches to tasks such as pick-and-place or grasping in clutter are those that have opted for simple grippers or even suction systems instead of dexterous multi-fingered platforms. We argue that the reason for the success of these simple manipulation systemsis what we call passive stability: passive phenomena due to nonbackdrivable joints or underactuation allow for robust grasping without complex sensor feedback or controller design. While these effects are being leveraged to great effect, it appears the practical manipulation community lacks the tools to analyze them. In fact, we argue that the traditional grasp modeling theory assumes a complexity that most robotic hands do not possess and is therefore of limited applicability to the robotic hands commonly used today. We discuss these limitations of the existing grasp modeling literature and setout to develop our own tools for the analysis of passive effects in robotic grasping. We show that problems of this kind are difficult to solve due to the non-convexity of the Maximum Dissipation Principle (MDP), which is part of the Coulomb friction law. We show that for planar grasps the MDP can be decomposed into a number of piecewise convex problems, which can be solved for efficiently. Despite decades of research robotic manipulation systems outside of highlystructured industrial applications are still far from ubiquitous. Perhaps particularly curious is the fact that there appears to be a large divide between the theoretical grasp modeling literature and the practical manipulation community. Specifically, it appears that the most successful approaches to tasks such as pick-and-place or grasping in clutter are those that have opted for simple grippers or even suction systems instead of dexterous multi-fingered platforms. We argue that the reason for the success of these simple manipulation systemsis what we call passive stability: passive phenomena due to nonbackdrivable joints or underactuation allow for robust grasping without complex sensor feedback or controller design. While these effects are being leveraged to great effect, it appears the practical manipulation community lacks the tools to analyze them. In fact, we argue that the traditional grasp modeling theory assumes a complexity that most robotic hands do not possess and is therefore of limited applicability to the robotic hands commonly used today. We discuss these limitations of the existing grasp modeling literature and setout to develop our own tools for the analysis of passive effects in robotic grasping. We show that problems of this kind are difficult to solve due to the non-convexity of the Maximum Dissipation Principle (MDP), which is part of the Coulomb friction law. We show that for planar grasps the MDP can be decomposed into a number of piecewise convex problems, which can be solved for efficiently. We show that the number of these piecewise convex problems is quadratic in the number of contacts and develop a polynomial time algorithm for their enumeration. Thus, we present the first polynomial runtime algorithm for the determination of passive stability of planar grasps. For the spacial case we present the first grasp model that captures passive effects due to nonbackdrivable actuators and underactuation. Formulating the grasp model as a Mixed Integer Program we illustrate that a consequence of omitting the maximum dissipation principle from this formulation is the introduction of solutions that violate energy conservation laws and are thus unphysical. We propose a physically motivated iterative scheme to mitigate this effect and thus provide the first algorithm that allows for the determination of passive stability for spacial grasps with both fully actuated and underactuated robotic hands. We verify the accuracy of our predictions with experimental data and illustrate practical applications of our algorithm. We build upon this work and describe a convex relaxation of the Coulombfriction law and a successive hierarchical tightening approach that allows us to find solutions to the exact problem including the maximum dissipation principle. It is the first grasp stability method that allows for the efficient solution of the passive stability problem to arbitrary accuracy. The generality of our grasp model allows us to solve a wide variety of problems such as the computation of optimal actuator commands. This makes our framework a valuable tool for practical manipulation applications. Our work is relevant beyond robotic manipulation as it applies to the stability of any assembly of rigid bodies with frictional contacts, unilateral constraints and externally applied wrenches. Finally, we argue that with the advent of data-driven methods as well as theemergence of a new generation of highly sensorized hands there are opportunities for the application of the traditional grasp modeling theory to fields such as robotic in-hand manipulation through model-free reinforcement learning. We present a method that applies traditional grasp models to maintain quasi-static stability throughout a nominally model-free reinforcement learning task. We suggest that such methods can potentially reduce the sample complexity of reinforcement learning for in-hand manipulation.We show that the number of these piecewise convex problems is quadratic in the number of contacts and develop a polynomial time algorithm for their enumeration. Thus, we present the first polynomial runtime algorithm for the determination of passive stability of planar grasps

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

Improving Robotic Manipulation via Reachability, Tactile, and Spatial Awareness

Robotic grasping and manipulation remains an active area of research despite significant progress over the past decades. Many existing solutions still struggle to robustly handle difficult situations that a robot might encounter even in non-contrived settings.For example, grasping systems struggle when the object is not centrally located in the robot's workspace. Also, grasping in dynamic environments presents a unique set of challenges. A stable and feasible grasp can become infeasible as the object moves; this problem becomes pronounced when there are obstacles in the scene. This research is inspired by the observation that object-manipulation tasks like grasping, pick-and-place or insertion require different forms of awareness. These include reachability awareness -- being aware of regions that can be reached without self-collision or collision with surrounding objects; tactile awareness-- ability to feel and grasp objects just tight enough to prevent slippage or crushing the objects; and 3D awareness -- ability to perceive size and depth in ways that makes object manipulation possible. Humans use these capabilities to achieve a high level of coordination needed for object manipulation. In this work, we develop techniques that equip robots with similar sensitivities towards realizing a reliable and capable home-assistant robot. In this thesis we demonstrate the importance of reasoning about the robot's workspace to enable grasping systems handle more difficult settings such as picking up moving objects while avoiding surrounding obstacles. Our method encodes the notion of reachability and uses it to generate not just stable grasps but ones that are also achievable by the robot. This reachability-aware formulation effectively expands the useable workspace of the robot enabling the robot to pick up objects from difficult-to-reach locations. While recent vision-based grasping systems work reliably well achieving pickup success rate higher than 90\% in cluttered scenes, failure cases due to calibration error, slippage and occlusion were challenging. To address this, we develop a closed-loop tactile-based improvement that uses additional tactile sensing to deal with self-occlusion (a limitation of vision-based system) and adaptively tighten the robot's grip on the object-- making the grasping system tactile-aware and more reliable. This can be used as an add-on to existing grasping systems. This adaptive tactile-based approach demonstrates the effectiveness of closed-loop feedback in the final phase of the grasping process. To achieve closed-loop manipulation all through the manipulation process, we study the value of multi-view camera systems to improve learning-based manipulation systems. Using a multi-view Q-learning formulation, we develop a learned closed-loop manipulation algorithm for precise manipulation tasks that integrates inputs from multiple static RGB cameras to overcome self-occlusion and improve 3D understanding. To conclude, we discuss some opportunities/ directions for future work

Planification de prises pour la manipulation robotisée

Cette thèse propose une nouvelle approche pour l'analyse des prises. En se basant sur la théorie de l'axe central du torseur des forces de contact, nous avons développé une nouvelle condition nécessaire et suffisante pour qu'une prise soit en fermeture de force (force-closure). Pour le cas des prises planes à n points de contact, nous avons proposé une nouvelle méthode géométrique pour le test de la force-closure. Cet algorithme graphique est basé sur des calculs géométriques simples qui permettent de réduire d'une manière significative le coût de calcul par rapport aux schémas linéaires. En outre, une nouvelle formulation linéaire est proposée pour le test et la caractérisation d'une prise à n points de contact. Cet algorithme présente l'avantage d'être très simple du point de vue implémentation et rapide du point de vue temps de calcul. Afin de valider l'approche proposée, nous l'avons comparée avec les algorithmes basés sur le calcul de l'enveloppe convexe des torseurs primitifs de contact. Des implémentations de cet algorithme sont effectuées dans le démonstrateur ``Move3d'' du LAAS ainsi que dans le simulateur ``GraspIt''. Nous abordons ensuite la synthèse de prises qui définissent une force-closure. En premier lieu, nous avons proposé la formulation du problème de recherche de la configuration des points de contact assurant un maximum de stabilité de l'objet comme étant un problème d'optimisation sous contraintes. En second lieu, pour les prises robotisées, nous avons présenté une approche pour la recherche des prises stables d'objets 3D. Le planificateur de prises proposé permet de générer des prises faisables sans passer par le calcul de la cinématique inverse de la main mécanique. Cette approche exploite, sans aucune transformation géométrique, les modèles CAO des objets à saisir pour minimiser le temps de recherche des prises. Ce planificateur de prises utilise un algorithme de résolution basé sur la technique d'optimisation stochastique du recuit simulé. Cette méthode nous a permis de synthétiser des prises de bonne qualité d'objets complexes même dans des environnements encombrés d'obstacles. Pour illustrer l'efficacité de la démarche proposée, nous avons présenté des implémentations dans l'environnement de simulation ``GraspIt''.This thesis proposes a new approach for grasp analysis. Based on the theory of central axes of grasp wrench, we developed a new necessary and sufficient condition for n-finger grasps to achieve force-closure property. For n-finger planar grasps, we proposed a new graphical method for testing force-closure of arbitrary planar objects. The proposed geometric algorithm is very simple and requires low computational complexity. Thus, it can be used in real-time implementations and reduce significantly the computational cost compared to linear programming schemes. Further, based on friction-cone linearization, we formalized quantitative test of planar and spatial n-fingered force-closure grasps as a new linear programming problem. The proposed quantitative force-closure test offers a good metric of quality measurement without need to compute the convex hull of the primitive contact wrenches, which efficiently reduces the amount of computational time. Implementations were performed on ``Move3D'' and ``GraspIt'' simulation environments. For grasp synthesis, we formulated the computation of fingertips locations problem as an optimization problem under constraints. Furthermore, we presented an approach for finding appropriate stable grasps for a robotic hand on arbitrary objects. We used simulated annealing technique to synthesize suboptimal grasps of 3D objects. Through numerical simulations on arbitrary shaped objects, we showed that the proposed approach is able to compute good grasps for multifingered hands within reasonable computational time. The proposed grasp planner was implemented on ``GraspIt'' simulator

Structured manifolds for motion production and segmentation : a structured Kernel Regression approach

Steffen JF. Structured manifolds for motion production and segmentation : a structured Kernel Regression approach. Bielefeld (Germany): Bielefeld University; 2010