The 3-D vision system integrated dexterous hand

Most multifingered hands use a tendon mechanism to minimize the size and weight of the hand. Such tendon mechanisms suffer from the problems of striction and friction of the tendons resulting in a reduction of control accuracy. A design for a 3-D vision system integrated dexterous hand with motor control is described which overcomes these problems. The proposed hand is composed of three three-jointed grasping fingers with tactile sensors on their tips, a two-jointed eye finger with a cross-shaped laser beam emitting diode in its distal part. The two non-grasping fingers allow 3-D vision capability and can rotate around the hand to see and measure the sides of grasped objects and the task environment. An algorithm that determines the range and local orientation of the contact surface using a cross-shaped laser beam is introduced along with some potential applications. An efficient method for finger force calculation is presented which uses the measured contact surface normals of an object

Study of the urban evolution of Brasilia with the use of LANDSAT data

The urban growth of Brasilia within the last ten years is analyzed with special emphasis on the utilization of remote sensing orbital data and automatic image processing. The urban spatial structure and the monitoring of its temporal changes were focused in a whole and dynamic way by the utilization of MSS-LANDSAT images for June 1973, 1978 and 1983. In order to aid data interpretation, a registration algorithm implemented at the Interactive Multispectral Image Analysis System (IMAGE-100) was utilized aiming at the overlap of multitemporal images. The utilization of suitable digital filters, combined with the images overlap, allowed a rapid identification of areas of possible urban growth and oriented the field work. The results obtained permitted an evaluation of the urban growth of Brasilia, taking as reference the proposed stated for the construction of the city

Vision-based Intelligent Prosthetic Robotic Arm

This project involves the design and development of an anthropomorphic trans-radial prosthetic robot that offers low-level assistive intelligence. The device is capable of autonomously determining object positions through a palm-embedded camera, and utilizes a two degree-of-freedom wrist to track and orient itself to an object. The fingers are actuated using a bevel-gear driven system that allows for variable grip strength through the relation between the forces generated at the fingertips and the current draw of the motors. The core advantage of this system is its user-friendly human interaction, reducing cognitive burden during grasping activities of daily living

Learning Latent Space Dynamics for Tactile Servoing

To achieve a dexterous robotic manipulation, we need to endow our robot with tactile feedback capability, i.e. the ability to drive action based on tactile sensing. In this paper, we specifically address the challenge of tactile servoing, i.e. given the current tactile sensing and a target/goal tactile sensing --memorized from a successful task execution in the past-- what is the action that will bring the current tactile sensing to move closer towards the target tactile sensing at the next time step. We develop a data-driven approach to acquire a dynamics model for tactile servoing by learning from demonstration. Moreover, our method represents the tactile sensing information as to lie on a surface --or a 2D manifold-- and perform a manifold learning, making it applicable to any tactile skin geometry. We evaluate our method on a contact point tracking task using a robot equipped with a tactile finger. A video demonstrating our approach can be seen in https://youtu.be/0QK0-Vx7WkIComment: Accepted to be published at the International Conference on Robotics and Automation (ICRA) 2019. The final version for publication at ICRA 2019 is 7 pages (i.e. 6 pages of technical content (including text, figures, tables, acknowledgement, etc.) and 1 page of the Bibliography/References), while this arXiv version is 8 pages (added Appendix and some extra details

Grasping bulky objects with two anthropomorphic hands

© 2016 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 worksThis paper presents an algorithm to compute precision grasps for bulky objects using two anthropomorphic hands. We use objects modeled as point clouds obtained from a sensor camera or from a CAD model. We then process the point clouds dividing them into two set of slices where we look for sets of triplets of points. Each triplet must accomplish some physical conditions based on the structure of the hands. Then, the triplets of points from each set of slices are evaluated to find a combination that satisfies the force closure condition (FC). Once one valid couple of triplets have been found the inverse kinematics of the system is computed in order to know if the corresponding points are reachable by the hands, if so, motion planning and a collision check are performed to asses if the final grasp configuration of the system is suitable. The paper inclu des some application examples of the proposed approachAccepted versio

Data-driven optimization for underactuated robotic hands

Passively adaptive and underactuated robotic hands have shown the potential to achieve reliable grasping in unstructured environments without expensive mechanisms or sensors. Instead of complex run-time algorithms, such hands use design-time analysis to improve performance for a wide range of tasks. Along these directions, we present an optimization framework for underactuated compliant hands. Our approach uses a pre-defined set of grasps in a quasistatic equilibrium formulation to compute the actuation mechanism design parameters that provide optimal performance. We apply our method to a class of tendon-actuated hands; for the simplified design of a two-fingered gripper, we show how a global optimum for the design optimization problem can be computed. We have implemented the results of this analysis in the construction of a gripper prototype, capable of a wide range of grasping tasks over a variety of objects

Reliable robotic handovers through tactile sensing

Joint manipulation and object exchange are common in many everyday scenarios. Although they are trivial tasks for humans, they are still very challenging for robots. Existing approaches for robot-to-human object handover assume that there is no fault during the transfer. However, unintentional perturbation forces can be occasionally applied to the object, resulting in the robot and the object being damaged, for example by being dropped. In this paper we present a novel approach to handover objects in a reliable manner while ensuring the safety of the robot and the object. Relying on tactile sensing, the system uses an effort controller to adapt the grasp forces in the presence of perturbations. Moreover, the proposed approach identifies a perturbation being applied on the object. When a perturbation event is detected, the algorithm classifies the direction of the pulling forces to decide whether to release it or not. The reliable handover system was implemented using a Shadow Robot hand equipped with BioTAC tactile sensors. Our results show that the system correctly adapts to the forces applied on the object to maintain the grasp and only releases the object if the human receiver pulls in the right direction