Obstacle detection for autonomous systems using stereoscopic images and bacterial behaviour

This paper presents a low cost strategy for real-time estimation of the position of obstacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation

A novel visual tracking scheme for unstructured indoor environments

In the ever-expanding sphere of assistive robotics, the pressing need for advanced methods capable of accurately tracking individuals within unstructured indoor settings has been magnified. This research endeavours to devise a realtime visual tracking mechanism that encapsulates high performance attributes while maintaining minimal computational requirements. Inspired by the neural processes of the human brain’s visual information handling, our innovative algorithm employs a pattern image, serving as an ephemeral memory, which facilitates the identification of motion within images. This tracking paradigm was subjected to rigorous testing on a Nao humanoid robot, demonstrating noteworthy outcomes in controlled laboratory conditions. The algorithm exhibited a remarkably low false detection rate, less than 4%, and target losses were recorded in merely 12% of instances, thus attesting to its successful operation. Moreover, the algorithm’s capacity to accurately estimate the direct distance to the target further substantiated its high efficacy. These compelling findings serve as a substantial contribution to assistive robotics. The proficient visual tracking methodology proposed herein holds the potential to markedly amplify the competencies of robots operating in dynamic, unstructured indoor settings, and set the foundation for a higher degree of complex interactive tasks

Acoustic event characterization for service robot using convolutional networks

This paper presents and discusses the creation of a sound event classification model using deep learning. In the design of service robots, it is necessary to include routines that improve the response of both the robot and the human being throughout the interaction. These types of tasks are critical when the robot is taking care of children, the elderly, or people in vulnerable situations. Certain dangerous situations are difficult to identify and assess by an autonomous system, and yet, the life of the users may depend on these robots. Acoustic signals correspond to events that can be detected at a great distance, are usually present in risky situations, and can be continuously sensed without incurring privacy risks. For the creation of the model, a customized database is structured with seven categories that allow to categorize a problem, and eventually allow the robot to provide the necessary help. These audio signals are processed to produce graphical representations consistent with human acoustic identification. These images are then used to train three convolutional models identified as high-performing in this type of problem. The three models are evaluated with specific metrics to identify the best-performing model. Finally, the results of this evaluation are discussed and analyzed

Fuzzy control of synchronous buck converters utilizing fuzzy inference system for renewable energy applications

In the present research, an innovative fuzzy control approach is developed specifically for synchronous buck converters utilized in renewable energy applications. The proposed control strategy effectively manages load changes, nonlinear loads, and input voltage variations while improving both stability and transient response. The method employs a fuzzy inference system (FIS) that integrates adaptive control, feedforward control, and multivariable control to guarantee optimal performance under a wide range of operating conditions. The design of the control scheme involves formulating a rule base connecting input variables to an output variable, which signifies the duty cycle of the switching signal. The rule base is configured to dynamically modify control rules and membership functions in accordance with load conditions, input voltage fluctuations, and other contributing factors. The performance of the control scheme is evaluated in comparison to conventional techniques, such as proportional integral derivative (PID) control. Results indicate that the advanced fuzzy control approach surpasses traditional methods in terms of voltage regulation, stability, and transient response, particularly when faced with variable load conditions and input voltage changes. As a result, this control scheme is highly compatible with renewable energy systems, encompassing solar and wind power installations where input voltage and load conditions may experience considerable fluctuations. This research highlights the potential of the proposed fuzzy control approach to significantly enhance the performance and reliability of renewable energy systems

Comparative study of optimization algorithms on convolutional network for autonomous driving

he last 10 years have been the decade of autonomous vehicles. Advances in intelligent sensors and control schemes have shown the possibility of real applications. Deep learning, and in particular convolutional networks have become a fundamental tool in the solution of problems related to environment identification, path planning, vehicle behavior, and motion control. In this paper, we perform a comparative study of the most used optimization strategies on the convolutional architecture residual neural network (ResNet) for an autonomous driving problem as a previous step to the development of an intelligent sensor. This sensor, part of our research in reactive systems for autonomous vehicles, aims to become a system for direct mapping of sensory information to control actions from real-time images of the environment. The optimization techniques analyzed include stochastic gradient descent (SGD), adaptive gradient (Adagrad), adaptive learning rate (Adadelta), root mean square propagation (RMSProp), Adamax, adaptive moment estimation (Adam), nesterov-accelerated adaptive moment estimation (Nadam), and follow the regularized leader (Ftrl). The training of the deep model is evaluated in terms of convergence, accuracy, recall, and F1-score metrics. Preliminary results show a better performance of the deep network when using the SGD function as an optimizer, while the Ftrl function presents the poorest performances

Evaluación – Prueba de habilidades prácticas CCNA.

El presente trabajo realizamos la prueba de habilidades prácticas para la evaluación de Diplomado de profundización CCNA con el fin de evaluar las competencias desarrollas en el transcurso del curso, haciendo uso de la herramienta Cisco Packet Tracer y elaborando un informe final que contenga la evidencia de la configuración de cada dispositivo con sus respectivas pruebas.The present work is the test of practical skills for the evaluation of CCNA deepening Diploma in order to assess the skills developed during the course, using the Cisco Packet Tracer tool and preparing a final report containing the evidence of the configuration of each device with their respective tests

Analysis and assessment software for multi-user collaborative cognitive radio networks

Computer simulations are without a doubt a useful methodology that allows to explore research queries and develop prototypes at lower costs and timeframes than those required in hardware processes. The simulation tools used in cognitive radio networks (CRN) are undergoing an active process. Currently, there is no stable simulator that enables to characterize every element of the cognitive cycle and the available tools are a framework for discrete-event software. This work presents the spectral mobility simulator in CRN called “App MultiColl-DCRN”, developed with MATLAB’s app designer. In contrast with other frameworks, the simulator uses real spectral occupancy data and simultaneously analyzes features regarding spectral mobility, decision-making, multi-user access, collaborative scenarios and decentralized architectures. Performance metrics include bandwidth, throughput level, number of failed handoffs, number of total handoffs, number of handoffs with interference, number of anticipated handoffs and number of perfect handoffs. The assessment of the simulator involves three scenarios: the first and second scenarios present a collaborative structure using the multi-criteria optimization and compromise solution (VIKOR) decision-making model and the naïve Bayes prediction technique respectively. The third scenario presents a multi-user structure and uses simple additive weighting (SAW) as a decision-making technique. The present development represents a contribution in the cognitive radio network field since there is currently no software with the same features

Hybrid fuzzy-sliding grasp control for underactuated robotic hand

A major part of the success of human-robots integration requires the development of robotic platforms capable of interacting in human environments. Human beings have an environment designed for their physical and morphological capacity, robots must adapt to these conditions. This paper presents a fuzzy-sliding hybrid grasp control for a five-finger robotic hand. As a design principle, the scheme takes into account the minimum force required on the object to prevent the object from slipping. The robotic hand uses force sensors on each finger to determine the grasp state. The control is designed with two control surfaces, one when there is slippage, the other when there is no slippage. For each surface, control rules are defined and unified by means of a fuzzy inference block. The proposed scheme is evaluated in the laboratory for different objects, which include spherical and cylindrical elements. In all cases, an excellent grasp was observed without producing deformations in the fragile objects