Improving grasping forces during the manipulation of unknown objects

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMany of the solutions proposed for the object manipulation problem are based on the knowledge of the object features. The approach proposed in this paper intends to provide a simple geometrical approach to securely manipulate an unknown object based only on tactile and kinematic information. The tactile and kinematic data obtained during the manipulation is used to recognize the object shape (at least the local object curvature), allowing to improve the grasping forces when this information is added to the manipulation strategy. The approach has been fully implemented and tested using the Schunk Dexterous Hand (SDH2). Experimental results are shown to illustrate the efficiency of the approach.Peer ReviewedPostprint (author's final draft

Coordination of several robots based on temporal synchronization

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper proposes an approach to deal with the problem of coordinating multi-robot systems, in which each robot executes individually planned tasks in a shared workspace. The approach is a decoupled method that can coordinate the participating robots in on-line mode. The coordination is achieved through the adjustment of the time evolution of each robot along its original planned geometric path according to the movements of the other robots to assure a collision-free execution of their respective tasks. To assess the proposed approach different tests were performed in graphical simulations and real experiments.Postprint (published version

Dexterous manipulation of unknown objects using virtual contact points

The manipulation of unknown objects is a problem of special interest in robotics since it is not always possible to have exact models of the objects with which the robot interacts. This paper presents a simple strategy to manipulate unknown objects using a robotic hand equipped with tactile sensors. The hand configurations that allow the rotation of an unknown object are computed using only tactile and kinematic information, obtained during the manipulation process and reasoning about the desired and real positions of the fingertips during the manipulation. This is done taking into account that the desired positions of the fingertips are not physically reachable since they are located in the interior of the manipulated object and therefore they are virtual positions with associated virtual contact points. The proposed approach was satisfactorily validated using three fingers of an anthropomorphic robotic hand (Allegro Hand), with the original fingertips replaced by tactile sensors (WTS-FT). In the experimental validation, several everyday objects with different shapes were successfully manipulated, rotating them without the need of knowing their shape or any other physical property.Peer ReviewedPostprint (author's final draft

Manipulación diestra de objetos desconocidos usando puntos de contacto virtuales

En este trabajo se presenta una estrategia de manipulación que permite rotar objetos desconocidos usando una mano robótica equipada con sensores táctiles. Las configuraciones de la mano que permiten cambiar la posición del objeto se calculan usando la información táctil y cinemática que se obtiene mientras se manipula el objeto, y razonando en base a las posiciones deseadas y reales de las yemas de los dedos durante la manipulación, teniendo en cuenta que las primeras no son físicamente alcanzables al estar situadas en el interior del objeto y son por lo tanto posiciones virtuales que tienen asociados puntos de contacto virtuales. El enfoque propuesto fue probado exitosamente usando tres dedos de una mano robótica antropomorfa (Allegro Hand), cuyas puntas de los dedos han sido modificadas para incluir los sensores táctiles (WTS-FT). En la validación experimental se manipularon exitosamente varios objetos de uso cotidiano de diferentes formas, rotándolos satisfactoriamente sin necesidad de conocer su forma.Postprint (author's final draft

Robust dexterous telemanipulation following object-orientation commands

This paper aims to present a procedure to change the orientation of a grasped object using dexterous manipulation. The manipulation is controlled by teleoperation in a very simple way, with the commands introduced by an operator using a keyboard. Design/methodology/approach - The paper shows a teleoperation scheme, hand kinematics and a manipulation strategy to manipulate different objects using the Schunk Dexterous Hand (SDH2). A state machine is used to model the teleoperation actions and the system states. A virtual link is used to include the contact point on the hand kinematics of the SDH2. Findings - Experiments were conducted to evaluate the proposed approach with different objects, varying the initial grasp configuration and the sequence of actions commanded by the operator. Originality/value - The proposed approach uses a shared telemanipulation schema to perform dexterous manipulation; in this schema, the operator sends high-level commands and a local system uses this information, jointly with tactile measurements and the current status of the system, to generate proper setpoints for the low-level control of the fingers, which may be a commercial close one. The main contribution of this work is the mentioned local system, simple enough for practical applications and robust enough to avoid object falls.Postprint (author's final draft

Herramienta para visualización gráfica de fuerzas de contacto y de movimientos de una mano robótica con sensores táctiles

En este trabajo se presenta una herramienta de software que permite la visualización gráfica de fuerzas de contacto de especial utilidad en prensiones usando manos robóticas. Las fuerzas visualizadas se generan debido al contacto entre las yemas de los dedos y el objeto manipulado, o debido a los pares aplicados en las articulaciones de los dedos. La herramienta también permite mover la mano real, ya sea especificando configuraciones de la mano o mediante instrucciones individuales para cada una de las articulaciones, el movimiento es ejecutado al mismo tiempo que se visualiza en la simulación gráfica. Como ejemplo práctico, se presenta una aplicación usando la mano Allegro con sensores táctiles WTS-FT y se muestran diferentes ejemplos de prensión de objetos con la información táctil registrada en los sensores táctiles y las fuerzas calculadas usando los pares aplicados por los motoresPostprint (author's final draft

Planning manipulation movements of a dual-arm system considering obstacle removing

The paper deals with the problem of planning movements of two hand-arm robotic systems, considering the possibility of using the robot hands to remove potential obstacles in order to obtain a free access to grasp a desired object. The approach is based on a variation of a Probabilistic Road Map that does not rule out the samples implying collisions with removable objects but instead classifies them according to the collided obstacle(s), and allows the search of free paths with the indication of which objects must be removed from the work-space to make the path actually valid; we call it Probabilistic Road Map with Obstacles (PRMwO). The proposed system includes a task assignment system that distributes the task among the robots, using for that purpose a precedence graph built from the results of the PRMwO. The approach has been implemented for a real dual-arm robotic system, and some simulated and real running examples are presented in the paper. (C) 2014 Elsevier B.V. All rights reserved.Postprint (published version

Robust dexterous telemanipulation following object-orientation commands

This paper aims to present a procedure to change the orientation of a grasped object using dexterous manipulation. The manipulation is controlled by teleoperation in a very simple way, with the commands introduced by an operator using a keyboard. Design/methodology/approach - The paper shows a teleoperation scheme, hand kinematics and a manipulation strategy to manipulate different objects using the Schunk Dexterous Hand (SDH2). A state machine is used to model the teleoperation actions and the system states. A virtual link is used to include the contact point on the hand kinematics of the SDH2. Findings - Experiments were conducted to evaluate the proposed approach with different objects, varying the initial grasp configuration and the sequence of actions commanded by the operator. Originality/value - The proposed approach uses a shared telemanipulation schema to perform dexterous manipulation; in this schema, the operator sends high-level commands and a local system uses this information, jointly with tactile measurements and the current status of the system, to generate proper setpoints for the low-level control of the fingers, which may be a commercial close one. The main contribution of this work is the mentioned local system, simple enough for practical applications and robust enough to avoid object falls.Postprint (author's final draft

Manipulation of unknown objects to improve the grasp quality using tactile information

This work presents a novel and simple approach in the area of manipulation of unknown objects considering both geometric and mechanical constraints of the robotic hand. Starting with an initial blind grasp, our method improves the grasp quality through manipulation considering the three common goals of the manipulation process: improving the hand configuration, the grasp quality and the object positioning, and, at the same time, prevents the object from falling. Tactile feedback is used to obtain local information of the contacts between the fingertips and the object, and no additional exteroceptive feedback sources are considered in the approach. The main novelty of this work lies in the fact that the grasp optimization is performed on-line as a reactive procedure using the tactile and kinematic information obtained during the manipulation. Experimental results are shown to illustrate the efficiency of the approachPeer ReviewedPostprint (published version

Object manipulation based on tactile information

In-hand dexterous manipulation of an object is the ability to change the configuration (position and/or orientation) of an object held in the hand. This is an ability that has allowed humans to use tools and interact with the environment effectively. For the past decades, robotics researchers have worked to provide dexterous manipulation skills to the robots by designing robotic hands that mimic the human hand and by developing applications that allow performing autonomous manipulation or teleoperation in harsh environments. Despite the progress made, managing the uncertainties that exist in the real world is one of the problems that still need to be worked on. Many existing manipulation methods for controlling robotic hands require a priori information about the object and high-fidelity sensors that are typically limited only to laboratory settings. The main objective of this thesis is to develop strategies for the dexterous manipulation of unknown objects, using the tactile information generated during the grasp of the object and the manipulation process itself. In manipulation applications based on tactile information, the robotic hand has access only to tactile and proprioceptive data, in addition, no a priori information is known about the manipulated object. This reflects real-world applications, where there is uncertainty in the models of the objects that are commonly manipulated in daily activities, as well as in the sensorial measurements. In this dissertation, novel manipulation strategies based on heuristic and gradient optimization methods are proposed. Three quality indexes are selected to measure the goodness of the grasp during the manipulation, related to the configuration of the hand, the quality of the grasp, and the configuration of the object. Starting from a given initial grasp, the manipulation strategies are able to improve one quality index or a combination of them. The manipulation strategies are validated with real experimentation using robotic hands equipped with tactile sensors, allowing the execution of practical applications, such as object recognition, force optimization, and telemanipulation.La manipulación diestra es la capacidad de cambiar la configuración (posición y/u orientación) de un objeto mientras es sostenido en la mano. Esta es una habilidad que ha permitido a los humanos usar herramientas e interactuar con el medio ambiente de forma efectiva. En las últimas décadas, los investigadores en robótica han trabajado para proporcionar la capacidad de ejercer manipulación diestra a los robots mediante el diseño de manos robóticas que imitan a la mano humana y mediante el desarrollo de aplicaciones que permiten realizar manipulación autónoma o teleoperación en entornos hostiles. A pesar de los avances logrados, gestionar las incertidumbres que existen en el mundo real es uno de los problemas en los que aún hay que seguir trabajando. Muchos enfoques existentes para controlar las manos robóticas, requieren información a priori sobre el objeto manipulado y sensores de alta fidelidad que normalmente se encuentran solo en laboratorios. El principal objetivo de esta tesis es desarrollar estrategias para la manipulación diestra de objetos desconocidos, utilizando la información táctil generada durante la prensión del objeto y el propio proceso de manipulación. En aplicaciones de manipulación basadas en información táctil, la mano robótica tiene acceso solo a datos táctiles y propioceptivos, además, no se conoce información a priori sobre el objeto manipulado. Esto va acorde con el mundo real, donde hay incertidumbre en los modelos de los objetos que se manipulan, así como en las mediciones de los sensores. En esta tesis se proponen nuevas estrategias de manipulación basadas en métodos heurísticos y de optimización del gradiente. Se eligieron tres índices para medir la calidad de la prensión durante la manipulación, los cuales están relacionados con la configuración de la mano, la calidad de la prensión y la configuración del objeto. A partir de una prensión inicial dada, las estrategias de manipulación pueden mejorar un índice de calidad o una combinación de ellos. Las estrategias de manipulación han sido validadas en experimentación real utilizando manos robóticas equipadas con sensores táctiles, permitiendo la ejecución de aplicaciones prácticas, como el reconocimiento de objetos, la optimización de fuerzas y la telemanipulación.Postprint (published version