Simplified Hand Configuration for Object Manipulation

This work is focused on obtaining realistic human hand models that are suitable for manipulation tasks. Firstly, a 24 DOF kinematic model of the human hand is defined. This model is based on the human skeleton. Intra-finger and inter-finger constraints have been included in order to improve the movement realism. Secondly, two simplified hand descriptions (9 and 6 DOF) have been developed according to the constraints predefined. These simplified models involve some errors in reconstructing the hand posture. These errors are calculated with respect to the 24 DOF model and evaluated according to the hand gestures. Finally, some criteria are defined by which to select the hand description best suited to the features of the manipulation task

Development of a deep learning model for recognising traffic sings focused on difficult cases

The automotive industry is expanding its efforts to develop new techniques for increasing the level of intelligent driving and create new autonomous cars capable of driving with more intelligent capabilities. Thus, companies in this sector are turning to the development of autonomous cars and more specifically developing software along with more artificial intelligent algorithms. However, to be able to trust these systems, they must be developed very carefully, and use techniques that can increase the level of recognition that will consequently improve the level of safety. One of the most important components in this respect for road users is the correct interpretation of traffic sings. This paper presents a deep learning model based on convolutional neural networks and image processing that can be used to improve the recognition of traffic sings autonomously. The results are focused on difficult cases such as images with lighting problems, blurry traffic sings, hidden traffic sings, and small images. Hence, real cases are used in this study for identifying the existing problems and achieving good performance in traffic signal recognition. Finally, as a result, the configuration of the neural architecture based on three phases of convolutions proposed shows a validation accuracy of 99.3% during the data training. Another comparison carried out with the model ResNet-50 obtained an accuracy of 88.5%. Thus, for this type of application, a high validation accuracy is required as the results of our model demonstrated

Decision making algorithm for an autonomous guide-robot using fuzzy logic

This paper presents a novel method to generate optimal presentations for a Guide-Robot that explains the exhibition to different types of audience. The generation of automatic presentations are selected dynamically regarding different criteria and an intelligent algorithm is implemented based on fuzzy logic to decide which presentation is the optimal. Thus, the decision-making mechanism prioritizes values of the presentation by means of a quality index that the fuzzy logic algorithm generates. The learning phase is produced using feedback information from the public that can modify the previous quality criteria to evaluate if the task is good or bad. Thus, the robot can learn by means of the interaction of the public and with the combination of fuzzy logic for selecting the optimal time and presentation that require a specific guided visit. To ensure that the learning phase is working properly, the robot has been tested in museums where there are interactions between the public and the robot

Fuzzy logic expert system for selecting robotic hands using kinematic parameters

Industry 4.0 is the current industrial revolution and robotics is an important factor for carrying out high dexterity manipulations. However, mechatronic systems are far from human capabilities and sophisticated robotic hands are highly priced. This paper describes a Fuzzy Logic Expert System (FLES) to map kinematic parameters from robotic hand features to the level of dexterity. The final goal is to obtain the adequate robotic hand that can do ranges of specific tasks according to the level of dexterity required. The FLES uses important kinematic parameters of the human hand/robotic hand: number of fingers, number of Degrees of Freedom (DoF), and number of contacts that grasping involves. As a result, several robotic hands are evaluated using the FLES to determine the type of dexterity task that corresponds to each robotic hand

Design of a virtual assistant to improve interaction between the audience and the presenter

This article presents a novel design of a Virtual Assistant as part of a human-machine interaction system to improve communication between the presenter and the audience that can be used in education or general presentations for improving interaction during the presentations (e.g., auditoriums with 200 people). The main goal of the proposed model is the design of a framework of interaction to increase the level of attention of the public in key aspects of the presentation. In this manner, the collaboration between the presenter and Virtual Assistant could improve the level of learning among the public. The design of the Virtual Assistant relies on non-anthropomorphic forms with ‘live’ characteristics generating an intuitive and self-explainable interface. A set of intuitive and useful virtual interactions to support the presenter was designed. This design was validated from various types of the public with a psychological study based on a discrete emotions’ questionnaire confirming the adequacy of the proposed solution. The human-machine interaction system supporting the Virtual Assistant should automatically recognize the attention level of the audience from audiovisual resources and synchronize the Virtual Assistant with the presentation. The system involves a complex artificial intelligence architecture embracing perception of high-level features from audio and video, knowledge representation, and reasoning for pervasive and affective computing and reinforcement learning to teach the intelligent agent to decide on the best strategy to increase the level of attention of the audience

Haptic Manipulation of Deformable Objects in Hybrid Bilateral Teleoperation System

The aim of this work is the integration of a virtual environment containing a deformable object, manipulated by an open kinematical chain virtual slave robot, to a bilateral teleoperation scheme based on a real haptic device. The virtual environment of this hybrid bilateral teleoperation system combines collision detection algorithms, dynamical, kinematical and geometrical models with a position–position and/or force–position bilateral control algorithm, to produce on the operator side the reflected forces corresponding to the virtual mechanical interactions, through a haptic device. Contact teleoperation task over the virtual environment with a flexible object is implemented and analysed

Debilidad de la voluntad y dominio racional. [RESEÑA]

Ignacio SERRANO DEL POZO, Debilidad de la voluntad y dominio racional. El problema de la incontinencia y la continencia en la filosofía de Tomás de Aquino, Pamplona: Eunsa («Colección de Pensamiento medieval y renacentista», 135), 2013, 254 pp