Intelligent collision avoidance system for industrial manipulators

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThe new paradigm of Industry 4.0 demand the collaboration between robot and humans. They could help (human and robot) and collaborate each other without any additional security, unlike other conventional manipulators. For this, the robot should have the ability of acquire the environment and plan (or re-plan) on-the-fly the movement avoiding the obstacles and people. This work proposes a system that acquires the space of the environment, based on a Kinect sensor, verifies the free spaces generated by a Point Cloud and executes the trajectory of manipulators in these free spaces. The simulation system should perform the path planning of a UR5 manipulator for pick-and-place tasks, while avoiding the objects around it, based on the point cloud from Kinect. And due to the results obtained in the simulation, it was possible to apply this system in real situations. The basic structure of the system is the ROS software, which facilitates robotic applications with a powerful set of libraries and tools. The MoveIt! and Rviz are examples of these tools, with them it was possible to carry out simulations and obtain planning results. The results are reported through logs files, indicating whether the robot motion plain was successful and how many manipulator poses were needed to create the final movement. This last step, allows to validate the proposed system, through the use of the RRT and PRM algorithms. Which were chosen because they are most used in the field of robot path planning.Os novos paradigmas da Indústria 4.0 exigem a colaboração entre robôs e seres humanos. Estes podem ajudar e colaborar entre si sem qualquer segurança adicional, ao contrário de outros manipuladores convencionais. Para isto, o robô deve ter a capacidade de adquirir o meio ambiente e planear (ou re-planear) on-the-fly o movimento evitando obstáculos e pessoas. Este trabalho propõe um sistema que adquire o espaço do ambiente através do sensor Kinect. O sistema deve executar o planeamento do caminho de manipuladores que possuem movimentos de um ponto a outro (ponto inicial e final), evitando os objetos ao seu redor, com base na nuvem de pontos gerada pelo Kinect. E devido aos resultados obtidos na simulação, foi possível aplicar este sistema em situações reais. A estrutura base do sistema é o software ROS, que facilita aplicações robóticas com um poderoso conjunto de bibliotecas e ferramentas. O MoveIt! e Rviz são exemplos destas ferramentas, com elas foi possível realizar simulações e conseguir os resultados de planeamento livre de colisões. Os resultados são informados por meio de arquivos logs, indicando se o movimento do UR5 foi realizado com sucesso e quantas poses do manipulador foram necessárias criar para atingir o movimento final. Este último passo, permite validar o sistema proposto, através do uso dos algoritmos RRT e PRM. Que foram escolhidos por serem mais utilizados no ramo de planeamento de trajetória para robôs

Wireless sensor network for ignitions detection: an IoT approach

Wireless Sensor Networks (WSN) can be used to acquire environmental variables useful for decision-making, such as agriculture and forestry. Installing a WSN on the forest will allow the acquisition of ecological variables of high importance on risk analysis and fire detection. The presented paper addresses two types of WSN developed modules that can be used on the forest to detect fire ignitions using LoRaWAN to establish the communication between the nodes and a central system. The collaboration between these modules generate a heterogeneous WSN; for this reason, both are designed to complement each other. The first module, the HTW, has sensors that acquire data on a wide scale in the target region, such as air temperature and humidity, solar radiation, barometric pressure, among others (can be expanded). The second, the 5FTH, has a set of sensors with point data acquisition, such as flame ignition, humidity, and temperature. To test HTW and 5FTH, a LoRaWAN communication based on the Lorix One gateway is used, demonstrating the acquisition and transmission of forest data (simulation and real cases). Even in internal or external environments, these results allow validating the developed modules. Therefore, they can assist authorities in fighting wildfire and forest surveillance systems in decision-making.This work is financed by SAFe Project through PROMOVE—Fundação La Caixainfo:eu-repo/semantics/publishedVersio

Deep learning method to identify fire ignitions

The SAFe project aims to create and implement a set of innovative operations that minimize the time of forest ignitions identification contributing to the development of the Trás-os-Montes region. Thus, it is intended to locate a set of sensors in the forest, data information will be collected, and the artificial intelligence algorithm Deep Learning will be applied to achieve the intended end. Numerical results demonstrated the approach reliability.info:eu-repo/semantics/publishedVersio

Development of a dynamic path for a toxic substances mapping mobile robot in industry environment

Some industries have critical areas (dangerous or hazardous) where the presence of a human must be reduced or avoided. In some cases, there are areas where humans should be replaced by robots. The present work uses a robot with differential drive to scan an environment with known and unknown obstacles, defined in 3D simulation. It is important that the robot be able to make the right decisions about its way without the need of an operator. A solution to this challenge will be presented in this paper. The control application and its communication module with a simulator or a real robot are proposed. The robot can perform the scan, passing through all the waypoints arranged in a grid. The results are presented, showcasing the robot’s capacity to perform a viable trajectory without human intervention.Project ”TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” is financed by the North Portugal Regional Operational. Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). This work is also financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.info:eu-repo/semantics/publishedVersio

Industrial robotic arm in machining process aimed to 3D objects reconstruction

Industrial robots are a technology which is highly present in industry and can perform several tasks, namely machining activities. Different than CNC machines, which work with G-code and have available several software applications to generate the machine code, there is a lack of software for robotic arms, in addition to each application depending on its own language and software. This work studied a way to use different robotic arms for 3D part machining processes, to perform 3D objects reconstruction obtained through a low-cost 3D scanner. Dealing with the 3D reconstruction by integrating 3D acquisition and robotic milling with software available on the market, this paper presents a system that acquires and reconstructs a 3D object, in order to seek greater flexibility with lower initial investments and checking the applicability of robot arm in these tasks. For this, a 3D object is scanned and imported to a CAD/CAM software, to generate the machining toolpath, and a software application is used to convert the G-code into robot code. Several experiments were performed, using an ABB IRB 2600 robot arm, and the results of the machining process allowed to validate the G-code conversion and milling process using robotic arms, according to the proposed methodology. © 2021 IEEE.This work has been supported by FCT – Fundac¸ao para a ˜ Ciencia e Tecnologia within the Projects UIDB/50014/2020 ˆ and UIDB/05757/2020.info:eu-repo/semantics/publishedVersio

Real cockpit proposal for flight simulation with airbus A32x models: an overview description

This paper describes the several steps to build an elaborate flight simulator cockpit, where the hardware is designed based on Mechatronic principles and the proposed software was developed using agile methodologies to create a Cyber-Physical System (CPS). Furthermore, this research attempts to simulate the real environment from an aircraft as close as possible with a real scale developed cockpit. Based on this, the presented paper contributions include: (1) The implementation of a complex dynamic system such as a CPS, where the Mechatronic system is part of it; (2) The deployment of a scale model of an Airbus A32x aircraft (one of the most used), integrating into a mathematical model adapted to the operation of an aircraft flight simulation system, regarding the physical forces involved. This project is also used to captivate the students’ motivation to the areas of technology such as electronics and programming and permits its development as a student project and thesis. Results allow validating the proposed cockpit.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.info:eu-repo/semantics/publishedVersio

Optimum sensors allocation for a forest fires monitoring system

Every year forest fires destroy millions of hectares of land worldwide. Detecting forest fire ignition in the early stages is fundamental to avoid forest fires catastrophes. In this approach, Wireless Sensor Network is explored to develop a monitoring system to send alert to authorities when a fire ignition is detected. The study of sensors allocation is essential in this type of monitoring system since its performance is directly related to the position of the sensors, which also defines the coverage region. In this paper, a mathematical model is proposed to solve the sensor allocation problem. This model considers the sensor coverage limitation, the distance, and the forest density interference in the sensor reach. A Genetic Algorithm is implemented to solve the optimisation model and minimise the forest fire hazard. The results obtained are promising since the algorithm could allocate the sensor avoiding overlaps and minimising the total fire hazard value for both regions considered.This research received no external funding.info:eu-repo/semantics/publishedVersio

Hardware-in-the-loop simulation approach for the robot at factory lite competition proposal

Mobile robotic applications are increasing in several areas not only in industries but also service robots. The Industry 4.0 promoted even more the digitalization of factories that opened space for smart-factories implementation. Robotic competitions are a key to improve research and to motivate learning. This paper addresses a new competition proposal, the Robot@Factory Lite, in the scope of the Portuguese Robotics Open. Beyond the competition, a reference robot with all its components is proposed and a simulation environment is also provided. To minimize the gap between the simulation and the real implementation, an Hardware-in-the-loop technique is proposed that allows to control the simulation with a real Arduino board. Results show the same code, and hardware, can control both simulation model and real robot.info:eu-repo/semantics/publishedVersio

A comparison of A* and RRT* algorithms with dynamic and real time constraint scenarios for mobile robots

There is an increasing number of mobile robot applications. The demanding of the Industry 4.0 pushes the robotic areas in the direction of the decision. The autonomous robots should actually decide the path according to the dynamic environment. In some cases, time requirements must also be attended and require fast path planning methods. This paper addresses a comparison between well-known path planning methods using a realistic simulator that handles the dynamic properties of robot models including sensors. The methodology is implemented in SimTwo that allows to compare the A* and RRT* algorithms in different scenarios with dynamic and real time constraint scenarios.This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT – Fundac¸ao para ˜ a Ciencia e a Tecnologia (Portuguese Foundation ˆ for Science and Technology) as part of project UID/EEA/50014/2013.info:eu-repo/semantics/publishedVersio

Optimal sensors positioning to detect forest fire ignitions

Forests have been harassed by fire in recent years. Whether by human action or for other reasons, the burned area has increased harming fauna and flora. It is fundamental to detect an ignition early in order to firefighters fight the fire minimizing the fire impacts. The proposed Forest Monitoring System aims at improving the nature monitoring and to enhance the existing surveillance systems. A set of innovative operations is proposed that will allow to identify a forest ignition and also will monitor the fauna. For that, a set of sensors are being developed and placed in the forest to transmit data and identify forest fire ignition. This paper addresses a methodology that identifies the ideal positions to place the developed sensors in order to minimize the fire hazard. Some preliminary results are shown by a random algorithm that spread points to position sensor modules in areas with high risk of fire hazard.This work has been supported by FCT — Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/5757/2020.info:eu-repo/semantics/publishedVersio