A deep reinforcement learning strategy for autonomous robot flocking

Social behaviors in animals such as bees, ants, and birds have shown high levels of intelligence from a multi-agent system perspective. They present viable solutions to real-world problems, particularly in navigating constrained environments with simple robotic platforms. Among these behaviors is swarm flocking, which has been extensively studied for this purpose. Flocking algorithms have been developed from basic behavioral rules, which often require parameter tuning for specific applications. However, the lack of a general formulation for tuning has made these strategies difficult to implement in various real conditions, and even to replicate laboratory behaviors. In this paper, we propose a flocking scheme for small autonomous robots that can self-learn in dynamic environments, derived from a deep reinforcement learning process. Our approach achieves flocking independently of population size and environmental characteristics, with minimal external intervention. Our multi-agent system model considers each agent’s action as a linear function dynamically adjusting the motion according to interactions with other agents and the environment. Our strategy is an important contribution toward real-world flocking implementation. We demonstrate that our approach allows for autonomous flocking in the system without requiring specific parameter tuning, making it ideal for applications where there is a need for simple robotic platforms to navigate in dynamic environments

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

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

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

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

Diseño e implementación de una red Mesh sobre UHF para sistemas de emergencia

Tomando como referencia la necesidad del Fondo de Prevención y Atención de Emergencias FOPAE de hacer un monitoreo permanente en la ciudad de Bogotá, y más específicamente, en zonas con alta vulnerabilidad de desastre natural, tal como lo es, el deslizamiento de tierra, se pensó en realizar una red de comunicación para enviar mediciones captadas por sensores de vibración terrestre en zonas de alto riesgo de deslizamientos de tierra. Para lograr esto, se realizó un estudio de factibilidad tecnológica, con el fin de encontrar dispositivos electrónicos de telecomunicación que faciliten efectuar la conexión a una frecuencia específica en la banda UHF (300 MHz - 3 GHz), analizando diferentes factores, tales como: potencia de transmisión, capacidad de tráfico de datos, velocidad de transmisión, distancias de cobertura, directividad de antenas, latencias del tráfico, etc. Luego de llevar a cabo este estudio, evaluando tecnologías como WI-FI, Bluetooth y XBee, se logró determinar que la tecnología XBee es la más apropiada para efectuar la conexión, toda vez que cuenta con dispositivos que transmiten y reciben datos en una frecuencia específica dentro del rango de UHF utilizando protocolos como ZigBee o DigiMesh (una derivación de ZigBee) y se ajustan a los requerimientos de tasa de datos y distancias por cubrir, lo cual se ve reflejado en la optimización de recursos para el desarrollo del proyecto

Transformation and dynamic visualization of images from computer through an FPGA in a matrix of LED

This article shows the implementation of a system that uses a graphic interface to load a digital image into a programmable logic device, which is stored in its internal RAM memory and is responsible for visualizing it in a matrix of RGB LEDs, so that This way, the LEDs show an equivalent to the image that was sent from the PC, conserving an aspect ratio and respecting as much as possible the color of the original image. To carry out this task, a Matlab script was designed to load the image, convert and format the data, which are transmitted to the FPGA using the RS232 protocol. The FPGA is in charge of receiving them, storing them and generating all the signals of control and synchronization of the system including the control of the PWM signals necessary to conserve the brightness of each one of the LEDs. This system allows the visualization of static images in standard formats and, in addition, thanks to the flexibility of the hardware used, it allows the visualization of moving images type GIF

Design of a controller for wheeled mobile robots based on automatic movement sequencing

There are many kinds of robots and among them the wheeled mobile robots (WMR) stand out, because they are relatively cheap and easy to build. These features make WMRs the test prototypes for control strategies or motion generation. In general, the controllers developed are based on sensory schemes that give an WMR the ability to travel through flat or obstructed environments. However, these strategies are highly reactive, i.e. they are based on the control-action scheme and are not adaptive; or, they are motion schemes built from simulations that assume the environmental conditions to determine the robot's path. In both cases, WMRs do not adapt perfectly to the change of environment, since the controller does not find appropriate movements for the  robot to move from one point to another. Therefore, this article proposesapartial solution to this problem, with a controller that generates sets of adaptive movements for an WMR to travel around its environment from the sensory perception information

Strategy to determine the foot plantar center of pressure of a person through deep learning neural networks

Some case studies treated by physiotherapists or orthopedists to measure the alignment of the lower extremities during a gait cycle are based on empirical methods of visual observation. This methodology does not guarantee total success, since it depends on the experience of the specialist, what can cause irreversible damage to patients, such as: hip displacement, wear and overload of the joints of a single lower limb. Although, this problem has been addressed in the investigation by means of devices implementation with sensors or methods of processing sequences of images and videos, this topic is still under investigation because the current methods depend on many external elements and data given by an expert in the area. Therefore, this paper proposes a partial solution to this problem by systematizing the experience of a specialist through a computational learning method