A novel robot co-worker system for paint factories without the need of existing robotic infrastructure

This paper presents a human–robot co-working system to be applied to industrial tasks such as the production line of a paint factory. The aim is to optimize the picking task with respect to manual operation in a paint factory. The use of an agile autonomous robot co-worker reduces the time in the picking process of materials, and the reduction of the exposure time to raw materials of the worker improves the human safety. Moreover, the process supervision is also improved thanks to a better traceability of the whole process. The whole system consists of a manufacturing process management system, an autonomous navigation system, and a people detection and tracking system. The localization module does not require the installation of reflectors or visual markers for robot operation, significantly simplifying the system deployment in a factory. The robot is able to respond to changing environmental conditions such as people, moving forklifts or unmapped static obstacles like pallets or boxes. The system is not tied to specific manufacturing orders. It is fully integrated with the manufacturing process management system and it can process all possible orders as long as their components are placed into the warehouse. Real experiments to validate the system have been performed in a paint factory by a real holonomic platform and a worker. The results are promising from the evaluation of performance indicators such as exposure time of the worker to raw materials, automation of the process, robust and safe navigation, and the assessment of the end-user.     

Framework to manage multiple goals in community-based energy sharing network in smart grid

Smart grid has opened up a new role of “prosumer” in an energy value network, transforming many conventional energy consumers into prosumers, who not only generate green energy but also share the surplus with utilities and other consumers. The concept of a goal-oriented prosumer community group (PCG) has emerged recently as an effective way to fulfill sustainable energy exchange. Such community-based energy sharing networks comprise multiple irreconcilable objectives such as demand constraints, cost constraints, and income maximization. In many cases, one goal may be achievable only at the expense of other goals. This necessitates the development of an effective framework to manage the multiple goals and reduce the gap with their achievement levels. Therefore, in this research paper, an effective framework is developed to negotiate among the multiple goals and thus to define optimal mutual goals for each PCG in a more sustainable manner using multiple-criteria goal programming techniques. Simulation results are presented to illustrate how the methods work in practical situations, where each of the objective measure is given a target value and the unwanted deviations from this set are minimized in an achievement function.     

Improved synergetic excitation control for transient stability enhancement and voltage regulation of power systems

This paper proposes decentralized improved synergetic excitation controllers (ISEC) for synchronous generators to enhance transient stability and obtain satisfactory voltage regulation performance of power systems. Each generator is considered as a subsystem, for which an ISEC is designed. According to the control objectives, a manifold, which is a linear combination of the deviation of generator terminal voltage, rotor speed and active power, is chosen for the design of ISEC. Compared with the conventional synergetic excitation controller (CSEC), a parameter adaptation scheme is proposed for updating the controller parameter online in order to improve the transient stability and voltage regulation performance simultaneously under various operating conditions. Case studies are undertaken on a single-machine infinite-bus power system and a two-area four-machine power system, respectively. Simulation results show the ISEC can provide better damping and voltage regulation performance, compared with the CSEC without parameter adaptation scheme and the conventional power system stabilizer.     

DFD-SS: Document Forgery Detection using Spectral – Spatial Features for Hyperspectral Images

In the present era of machines and edge-cutting technologies, still document frauds persist. They are done intuitively by using almost identical inks, that it becomes challenging to detect them—this demands an approach that efficiently investigates the document and leaves it intact. Hyperspectral imaging is one such a type of approach that captures the images from hundreds to thousands of spectral bands and analyzes the images through their spectral and spatial features, which is not possible by conventional imaging. Deep learning is an edge-cutting technology known for solving critical problems in various domains. Utilizing supervised learning imposes constraints on its usage in real scenarios, as the inks used in forgery are not known prior. Therefore, it is beneficial to use unsupervised learning. An unsupervised feature extraction through a Convolutional Autoencoder (CAE) followed by Logistic Regression (LR) for classification is proposed (CAE-LR). Feature extraction is evolved around spectral bands, spatial patches, and spectral-spatial patches. We inspected the impact of spectral, spatial, and spectral-spatial features by mixing inks in equal and unequal proportion using CAE-LR on the UWA writing ink hyperspectral images dataset for blue and black inks. Hyperspectral images are captured at multiple correlated spectral bands, resulting in information redundancy handled by restoring certain principal components. The proposed approach is compared with eight state-of-art approaches used by the researchers. The results depicted that by using the combination of spectral and spatial patches, the classification accuracy enhanced by 4.85% for black inks and 0.13% for blue inks compared to state-of-art results. In the present scenario, the primary area concern is to identify and detect the almost similar inks used in document forgery, are efficiently managed by the proposed approach.     

Aligning human psychomotor characteristics with robots,exoskeletons and augmented reality

In previous production literature, the uncertainty of human behaviour has been recognized as a source of productivity, quality, and safety problems. However, fundamental reasons for the uncertainty of human behavior have received little analysis in the production literature. Furthermore, potential for these fundamental reasons to be aligned with production technologies in order to improve production performance has not been addressed. By contrast, in this paper, fundamental reasons for the uncertainty of human behaviour are explained through a model of psychomotor characteristics that encompasses physiology, past experiences, personality, gender, culture, emotion, reasoning, and biocybernetics. Through reference to 10 action research cases, the formal model is applied to provide guidelines for planning production work that includes robots, exoskeletons, and augmented reality.     

A novel database of children's spontaneous facial expressions (LIRIS-CSE)

Computing environment is moving towards human-centered designs instead of computer centered designs and human's tend to communicate wealth of information through affective states or expressions. Traditional Human Computer Interaction (HCI) based systems ignores bulk of information communicated through those affective states and just caters for user's intentional input. Generally, for evaluating and benchmarking different facial expression analysis algorithms, standardized databases are needed to enable a meaningful comparison. In the absence of comparative tests on such standardized databases it is difficult to find relative strengths and weaknesses of different facial expression recognition algorithms. In this article we present a novel video database for Children's Spontaneous facial Expressions (LIRIS-CSE). Proposed video database contains six basic spontaneous facial expressions shown by 12 ethnically diverse children between the ages of 6 and 12 years with mean age of 7.3 years. To the best of our knowledge, this database is first of its kind as it records and shows spontaneous facial expressions of children. Previously there were few database of children expressions and all of them show posed or exaggerated expressions which are different from spontaneous or natural expressions. Thus, this database will be a milestone for human behavior researchers. This database will be a excellent resource for vision community for benchmarking and comparing results. In this article, we have also proposed framework for automatic expression recognition based on Convolutional Neural Network (CNN) architecture with transfer learning approach. Proposed architecture achieved average classification accuracy of 75% on our proposed database i.e. LIRIS-CSE.     

A comparison of fitness functions for identifying an LCD Glass-handling robot system

In this paper, system identification with variable fitness functions (FFs) by using the real-coded genetic algorithm (RGA) is proposed for a liquid crystal display (LCD) glass-handling robot system. The FFs including the state errors and energy balance equation are compared for the system. Firstly, the mechatronic modeling including mechanical and electrical dynamic equations are formulated. Secondly, the FFs including state errors and energy balance equations by using the RGA method are employed to maximize the FFs values, and to identify the unknown system parameters. From numerical simulations, it is found that the identified performance with energy balance equation FF by using the RGA method has the best identified performance among the other FFs. Finally, the RGA method with FFs are experimentally performed to identify the parameters for a real robot system. From the comparisons of the experimental results, it is also found that the energy balance equation FF has the best identified performance among the other FFs. Therefore, the unknown parameters of the real robot system can be correctly identified by the RGA method with the energy balance equation FF. The contribution of this paper is to propose an energetics FF to be implemented in system identification for the mechatronic system. It can be concluded that the more system states are used in the FF, the more correct system parameters are identified for the robot system.     

Double actuation architectures for rendering variable impedance in compliant robots: A review

Novel compliant actuation systems have been developed in recent years for a variety of possible advantages, such as establishing a safe human–robot interaction, increasing energy efficiency, reducing the effects of impacts and even for the development of neuro-inspired robotic platforms to be used in human motor control studies.In this rapidly growing and transversal research field, systems involving more than one active element (typically motors) for each actuated degree of freedom are being investigated to allow separate position and impedance regulations. Considering the wide range of applications and the large number of different arrangements deriving from the combination of two active elements and passive elastic components, several actuation architectures have been devised.This paper reviews state-of-the-art rotary variable impedance units incorporating two separate motors. Existing devices are grouped in three main categories. A critical and comparative analysis of the most relevant features is carried out, also based on most representative prototypes. Recently proposed methodologies and evaluation criteria for design optimization are illustrated and perspectives on potential applications of double actuation systems are presented.     

Energy shaping control of an inverted flexible pendulum fixed to a cart

Control of compliant mechanical systems is increasingly being researched for several applications including flexible link robots and ultra-precision positioning systems. The control problem in these systems is challenging, especially with gravity coupling and large deformations, because of inherent underactuation and the combination of lumped and distributed parameters of a nonlinear system. In this paper we consider an ultra-flexible inverted pendulum on a cart and propose a new nonlinear energy shaping controller to keep the pendulum at the upward position with the cart stopped at a desired location. The design is based on a model, obtained via the constrained Lagrange formulation, which previously has been validated experimentally. The controller design consists of a partial feedback linearization step followed by a standard PID controller acting on two passive outputs. Boundedness of all signals and (local) asymptotic stability of the desired equilibrium is theoretically established. Simulations and experimental evidence assess the performance of the proposed controller.     

An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles

Robust and performing navigation systems for Autonomous Underwater Vehicles (AUVs) play a discriminant role towards the success of complex underwater missions involving one or more AUVs. The quality of the filtering algorithm for the estimation of the AUV navigation state strongly affects the performance of the overall system. In this paper, the authors present a comparison between the Extended Kalman Filter (EKF) approach, classically used in the field of underwater robotics and an Unscented Kalman Filter (UKF). The comparison results to be significant as the two strategies of filtering are based on the same process and sensors models. The UKF-based approach, here adapted to the AUV case, demonstrates to be a good trade-off between estimation accuracy and computational load. UKF has not yet been extensively used in practical underwater applications, even if it turns out to be quite promising. The proposed results rely on the data acquired during a sea mission performed by one of the two Typhoon class vehicles involved in the NATO CommsNet13 experiment (held in September 2013). As ground truth for performance evaluation and comparison, performed offline, position measurements obtained through Ultra-Short BaseLine (USBL) fixes are used. The result analysis leads to identify both the strategies as effective for the purpose of being included in the control loop of an AUV. The UKF approach demonstrates higher performance encouraging its implementation as a more suitable navigation algorithm even if, up to now, it is still not used much in this field.