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

Robot Physical Interaction through the combination of Vision, Tactile and Force Feedback: Applications to Assistive Robotics

Robot manipulation is a great challenge; it encompasses versatility -adaptation to different situations-, autonomy -independent robot operation-, and dependability -for success under modeling or sensing errors. A complete manipulation task involves, first, a suitable grasp or contact configuration, and the subsequent motion required by the task. This monograph presents a unified framework by introducing task-related aspects into the knowledge-based grasp concept, leading to task-oriented grasps. Similarly, grasp-related issues are also considered during the execution of a task, leading to grasp-oriented tasks which is called framework for physical interaction (FPI). The book presents the theoretical framework for the versatile specification of physical interaction tasks, as well as the problem of autonomous planning of these tasks. A further focus is on sensor-based dependable execution combining three different types of sensors: force, vision and tactile. The FPI approach allows to perform a wide range of robot manipulation tasks. All contributions are validated with several experiments using different real robots placed on household environments; for instance, a high-DoF humanoid robot can successfully operate unmodeled mechanisms with widely varying structure in a general way with natural motions. This research was recipient of the European Georges Giralt Award and the Robotdalen Scientific Award Honorary Mention.  

Underwater Wireless Vision System Using Progressive Image Compression and Region of Interest

[ES] La creciente demanda en todo el mundo de sistemas de intervención submarina en diversos dominios de aplicación requiere sistemas más versátiles y económicos. Empleando un sistema de comunicación inalámbrica, los robots semiautónomos supervisados disponen de libertad de movimientos y, al mismo tiempo, permiten al operador obtener información visual y supervisar la intervención. Por otro lado, la velocidad de transmisión de datos típica de los canales inalámbricos submarinos es, en general, muy limitada, siendo necesaria la aplicación de técnicas de compresión avanzadas. En este artículo se presenta principalmente el algoritmo DEBT (Depth Embedded Block Tree) para la compresión progresiva de imágenes con región de interés (ROI). Los resultados demuestran ventajas con respecto a otros algoritmos de compresión, y la posibilidad de ejecución del algoritmo en tiempo real en ordenadores embebidos de bajo consumo basados en ARM.[EN] The increasing demand for underwater robotic intervention systems around the world in several application domains requires more versatile and inexpensive systems. By using a wireless communication system, supervised semi-autonomous robots have freedom of movement and, at the same time, allows the operator to get camera feedback and supervise the intervention. On the otherhand, the typical data rate of the wireless submarine channels is generally very limited, requiring the application of advanced compression techniques. In this paper we present the DEBT (Depth Embedded Block Tree) algorithm for the progressive compressionof images with region of interest (ROI). 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