Otimização por Enxame de Partículas aplicado à formação e atuação de grupos robóticos

In this paper it is described the modeling, implementation and evaluation of the Particle Swarm Optimization (PSO) efficiency when applied to robotic group formation and coordination. The robotic task is performed over a natural disaster, simulated as a forest fire. The robot squad mission is to surround the fire and avoid fire’s propagation. Experiments have been made with several parameter’s variation seeking to get the more efficient swarm performance. This paper describes all performed experiments detailing all sets of parameters, including positive and negative results. The simulation’s results showed that with an adequate set of parameters is possible to get satisfactory strategic positions for a multi-robotic system’s operation.Keywords: Particle Swarm Optimization, coordination, multi-robotics systems.Neste artigo, descreve-se o modelo, a implementação e a avaliação da eficiência de Algoritmos de Otimização por Enxame de Partículas aplicados à formação e atuação de grupos robóticos. A atuação do grupo robótico é realizada sobre um desastre ambiental do tipo incêndio florestal. São avaliados diversos parâmetros que influenciam o comportamento da otimização, como inércia, confiança, tipos de modelos sociais e tamanho de enxame. Descrevem-se as experiências realizadas, detalhando-se os conjuntos de parâmetros que permitem obter resultados positivos e também negativos. Os resultados das simulações demonstram que, com um conjunto adequado de parâmetros, é possível obter posições satisfatórias para atuação do grupo robótico. Palavras-chave: Otimização por Enxame de Partículas, coordenação, sistemas multirrobóticos

Exploiting the Use of Convolutional Neural Networks for Localization in Indoor Environments

Indoor localization has been an active research area for the last two decades. A great number of sensors have been applied in the task of localization—some with high computational and energy demands (e.g. laser beams), or with issues related to the coverage area, for example, by making use of images obtained by a network of cameras. A different approach, which presents less energy demands and a wide area of coverage, can be created by means of the signal strength of wireless networks. The open issue with signal strength is its high instability due to interferences, attenuation and fading, which, in general, makes the localization systems to present less than desired accuracy. In this article, we exploit the use of Convolutional Neural Networks (ConvNets) in the task of localization. The main motivation behind the employment of ConvNets is its inherent ability of feature extraction, which we believe can deal better with the noise without a filtering step. We evaluate how ConvNets can be employed and identify the best topologies that lead to the lowest errors

Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection

Cyber security is a topic of increasing relevance in relation to industrial networks. The higher intensity and intelligent use of data pushed by smart technology (Industry 4.0) together with an augmented integration between the operational technology (production) and the information technology (business) parts of the network have considerably raised the level of vulnerabilities. On the other hand, many industrial facilities still use serial networks as underlying communication system, and they are notoriously limited from a cyber security perspective since protection mechanisms available for TCP/IP communication do not apply. Therefore, an attacker gaining access to a serial network can easily control the industrial components, potentially causing catastrophic incidents, jeopardizing assets and human lives. This study proposes a framework to act as an anomaly detection system (ADS) for industrial serial networks. It has three ingredients: an unsupervised K-means component to analyse message content, a knowledge-based expert system component to analyse message metadata, and a voting process to generate alerts for security incidents, anomalous states, and faults. The framework was evaluated using the Profibus-DP, a network simulator which implements a serial bus system. Results for the simulated traffic were promising: 99.90% for accuracy, 99,64% for precision, and 99.28% for F1-Score. They indicate feasibility of the framework applied to serial-based industrial networks

Non-IP Industrial Networks: An Agnostic Anomaly Detection System

This paper describes a system to detect anomalies in non-IP (Internet Protocol) industrial networks on Industrial Control Systems (ICS). Non-IP industrial networks are widely applied in ICS to connect sensors and actuators to control systems or business networks. They were designed to be in an air-gapped security environment and therefore contain almost no cyber security features and are vulnerable to various attacks. Even though they are part of the communication layers, a few external cyber security controls are applied in this crucial tier. As an extension of the work by De Moura et al. (2021), this study proposes and tests the proof-of-concept of an agnostic anomaly detection system (AADS) to detect anomalies on any non-IP industrial network (e.g., DeviceNet, CANBus) as an additional cyber security measure working at the physical network layer. The proof-of-concept is comprised of three modules, including hardware and software components: data gathering (sniffer), parser, and detection. Testing the proof-of-concept in an industrial lab network (i.e., a Profibus-DP lab network) showed the proposal's feasibility with a detection rate above 99% (overall accuracy: 99.59%; F1-Score: 99.18%)

Cybersecurity in Industrial Networks: Artificial Intelligence Techniques Applied to Intrusion Detection Systems

Industrial control systems (ICS) operate on serial based networks which lack proper security safeguards by design. They are also becoming more integrated to corporate networks, creating new vulnerabilities which expose ICS networks to increasing levels of risk with potentially significant impact. Despite those risks, only a few mechanisms have been suggested and are available in practice as cybersecurity safeguards for the ICS network layer, maybe because they might not be commercially viable. Intrusion detection systems (IDS) are typically deployed in the corporate networks to protect against attacks since they are based on TCP/IP. However, IDS are not used in serial based ICS networks yet. This study examines and compares modern Artificial Intelligence (AI) techniques applied in IDS that are potentially useful for serial-based ICS networks. The results showed that current AI-based IDS methods are viable in such networks. A mix of AI techniques would be the best way forward to detect known attacks via rules and novel attacks, not previously mapped, via supervised and unsupervised techniques. Despite these strategies’ limited use in serial-based networks, their adoption could significantly strengthen cybersecurity of ICS networks

Fine-tuning of UAV control rules for spraying pesticides on crop fields

The use of pesticides in agriculture is essential to maintain the quality of large-scale production. The spraying of these products by using aircraft speeds up the process and prevents compacting of the soil. However, adverse weather conditions (e.g. the speed and direction of the wind) can impair the effectiveness of the spraying of pesticides in a target crop field. Thus, there is a risk that the pesticide can drift to neighboring crop fields. It is believed that a large amount of all the pesticide used in the world drifts outside of the target crop field and only a small amount is effective in controlling pests. However, with\ud increased precision in the spraying, it is possible to reduce the amount of pesticide used and improve the quality of agricultural products as well as mitigate the risk of environmental damage. With this objective, this paper proposes a methodology based on Particle Swarm Optimization (PSO) for the fine-tuning of control rules during the spraying of pesticides in crop fields. This methodology can be employed with speed and efficiency and achieve good results by taking account of the weather conditions reported by a Wireless Sensor Network (WSN). In this scenario, the UAV becomes a mobile node of the WSN that is able to make personalized decisions for each crop field. The experiments\ud that were carried out show that the optimization methodology proposed is able to reduce the drift of pesticides by fine-tuning of control rules.FAPESP (processes ID 2012/22550-0)Office of Naval Research Global (No. 62909-14-1-N241)CAPES (Capes Foundation, Ministry of Education of Brazil)CNPq (Brazilian National Counsel of Technological and Scientific Development

ACHORD: communication-aware multi-robot coordination with intermittent connectivity

© 2022 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 worksCommunication is an important capability for multi-robot exploration because (1) inter-robot communication (comms) improves coverage efficiency and (2) robot-to-base comms improves situational awareness. Exploring comms-restricted (e.g., subterranean) environments requires a multi-robot system to tolerate and anticipate intermittent connectivity, and to carefully consider comms requirements, otherwise mission-critical data may be lost. In this paper, we describe and analyze ACHORD (Autonomous & Collaborative High-Bandwidth Operations with Radio Droppables), a multi-layer networking solution which tightly co-designs the network architecture and high-level decision-making for improved comms. ACHORD provides bandwidth prioritization and timely and reliable data transfer despite intermittent connectivity. Furthermore, it exposes low-layer networking metrics to the application layer to enable robots to autonomously monitor, map, and extend the network via droppable radios, as well as restore connectivity to improve collaborative exploration. We evaluate our solution with respect to the comms performance in several challenging underground environments including the DARPA SubT Finals competition environment. Our findings support the use of data stratification and flow control to improve bandwidth-usage.Peer ReviewedPostprint (author's final draft

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Intelligent strategies applied to autonomous mobile robots and groups of robots

O contínuo aumento da complexidade no controle de sistemas robóticos, bem como a aplicação de grupos de robôs auxiliando ou substituindo seres humanos em atividades críticas tem gerado uma importante demanda por soluções mais robustas, flexíveis, e eficientes. O desenvolvimento convencional de algoritmos especializados, constituídos de sistemas baseados em regras e de autômatos usados para coordenar estes conjuntos físicos em um ambiente dinâmico é um desafio extremamente complexo. Diversos modelos de desenvolvimento existem, entretanto, muitos desafios da área da robótica móvel autônoma continuam em aberto. Esta tese se insere no contexto da busca por soluções inteligentes a serem aplicadas em robôs móveis autônomos com o objetivo de permitir a operação destes em ambientes dinâmicos. Buscamos, com a investigação e aplicação de estratégias inteligentes por meio de aprendizado de máquina no funcionamento dos robôs, a proposta de soluções originais que permitam uma nova visão sobre a operação de robôs móveis em três dos desafios da área da robótica móvel autônoma, que são: localização, navegação e operações com grupos de robôs. As pesquisas sobre localização e coordenação de grupos apresentam investigação e propostas originais, buscando estender o estado da arte, onde apresentam resultados inovadores. A parte sobre navegação tem como objetivo principal ser um elo entre os conceitos de localização e coordenação de grupos, sendo o foco o desenvolvimento de um veículo autônomo com maior implicação em avanços técnicos. Relacionado com a coordenação de grupos de robôs, fizemos a escolha de trabalhar sobre uma aplicação modelada como o problema de combate a incêndios florestais. Buscamos desenvolver um ambiente de simulação realístico, onde foram avaliadas quatro técnicas para busca de iii estratégias de formação do grupo: Algoritmos Genéticos, Otimização por Enxame de Partículas, Hill Climbing e (iv) Simulated Annealing. Com base nas diversas avaliações realizadas pudemos mostrar quais das técnicas e conjuntos de parâmetros permitem a obtenção de resultados mais acurados que os demais. Além disso, mostramos como uma heurística baseada em populações anteriores pode auxiliar na tolerância a falhas da operação. Relacionado com a tarefa de navegação, apresentamos o desenvolvimento de um veículo autônomo de grande porte funcional para ambientes externos. Buscamos aperfeiçoar uma arquitetura para navegação autônoma, baseada em visão monocular e com capacidade de seguir pontos esparsos de GPS. Mostramos como a simulação e os usos de robôs de pequeno porte auxiliaram no desenvolvimento do veículo de grande porte e apresentamos como as redes neurais podem ser aplicadas nos modelos de navegação autônoma. Na investigação sobre localização, mostramos um método utilizando informação obtida de redes sem fio para prover informação de localização para robôs móveis. As informações obtidas da rede sem fio são utilizadas para aprendizado da posição de um robô móvel por meio de uma rede neural. Diversas avaliações foram realizadas buscando entender o comportamento do sistema com diferentes números de pontos de acesso, com uso de filtros, com diferentes topologias. Os resultados mostram que o modelo usando redes sem fio pode ser um possível método prático e barato para localização de robôs móveis. Esta tese aborda temas relevantes e propostas originais relacionadas com os objetivos propostos, apresentando métodos que provenham autonomia na coordenação de grupos e nas atividades individuais dos mesmos. A busca por altos graus de eficiência na resolução de tarefas em ambientes dinâmicos ainda é um campo que carece de soluções e de um aprofundamento nas pesquisas. Sendo assim, esta pesquisa buscou agregar diversos avanços científicos na área de pesquisa de robôs móveis autônomos e coordenação de grupos, por meio da aplicação de estratégias inteligentesThe constant increasing of the complexity in the control of robotic systems, as well as the application of groups of robots assisting or replacing human beings in critical activities has generated a significant demand for more robust, flexible and efficient solutions. The conventional development of specialized algorithms consisted of rule-based systems and automatas, used to coordinate these physical sets in a dynamic environment is an extremely complex challenge. Although several models of development of robotic issues are currently in use, many challenges in the area remain open. This thesis is related to the search for intelligent strategies to be applied in autonomous mobile robots in order to allow practical operations in dynamic environments. We seek, with the investigation of intelligent strategies by means of the use of machine learning in the robots, to propose original solutions to allow contributions in three challenges of the robotic research area: localization, navigation and coordination of groups of robots. The investigations about localization and groups of robots show novel and original proposals, where we sought to extend the state of the art. The navigation part has as its major objective to be a link between the subjects of localization and navigation, being its aim to help the deployment of a autonomous vehicle implying in greater technical advances. Related to the robotic group coordination, we have made the choice to work on an application modeled as a wildfire combat operation. We have developed a simulation environment in which we have evaluated four techniques to obtain strategies for the group formation: genetic algorithms, particle swarm optimization, hill climbing and simulated annealing. The v results showed that we can have very different accuracy with different techniques and sets of parameters. Furthermore, we show how a heuristic based on the use of past populations can assist in fault tolerant operation. Related to the autonomous navigation task, we present the development of a large autonomous vehicle capable of operating in outdoor environments. We sought to optimize an architecture for autonomous navigation based on monocular vision and with the ability to follow scattered points of GPS.We show how the use of simulation and small robots could assist in the development of large vehicle. Furthermore, we show how neural networks can be applied as a controller to autonomous navigation systems. In the investigation about localization, we presented a method using wireless networks to provide information about localization to mobile robots. The information gathered by the wireless network is used as input in an artificial neural network which learns the position of the robot. Several evaluations were carried out in order to understand the behavior of the proposed system, as using different topologies, different numbers of access points and the use of filters. Results showed that the proposed system, using wireless networks and neural networks, may be a useful and easy to use solution for localization of mobile robots. This thesis has addressed original and relevant topics related to the proposed objectives, showing methods to allow degrees of autonomy in robotic operations. The search for higher degrees of efficiency in tasks solving in dynamic environments is still a field that lacks solutions. Therefore, this study sought to add several scientific contributions in the autonomous mobile robots research area and coordination of groups, by means of the application of intelligent strategie

Evaluation of a Fault-tolerant Model for Tactic Operations of Mobile Robotic Groups Using Genetic Algorithms

This paper addresses the evaluation of a fault-tolerant model for tactic operations of mobile robotic groups. The coordinated action of the group is planned with Genetic Algorithms (GA) and aims to act on an environmental disaster scenario, simulated as a forest fire. The robotic squad should surround the fire and avoid fire's propagation. Initially, we evaluate several parameters in the GA, seeking to obtain the set of parameters that would accomplish the more efficient evolution. Then, we simulate failures in robots operations in order to evaluate strategies of reorganization. The simulation's results1 showed that with an adequate set of parameters it is possible to get satisfactory strategic positions to coordinate and to reorganize the robotic group in case of robot failures