Sistema de navegação para veículos autônomos: aplicação em um contexto industrial

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.Aplicações envolvendo transporte de carga ou pessoas em ambientes industriais con- trolados fazem parte do contexto encontrado em diversas áreas do mercado. Nos dias atuais, nestas aplicações, observa-se um momento de transição da utilização de mão de obra humana ou robôs de movimentação limitada para a utilização de sistemas móveis capazes de se adaptar em ambientes dinâmicos. O objetivo deste trabalho é apresentar uma solução de sistema de navegação para veículos autônomos capa- zes de trafegar em ambientes industriais abertos, visando aplicações de transporte de carga. Durante o desenvolvimento, buscou-se basear a arquitetura da solução em sistemas reais. Sendo assim, realizou-se uma etapa de pesquisa no início do projeto. A solução final é apresentada em blocos, com um subsistema de localização e um subsistema de tomada de decisão. No subsistema de localização são utilizadas téc- nicas como odometria, SLAM e AMCL para gerar as informações necessárias para a atuação do sistema de tomada de decisão. O sistema de tomada de decisão traz con- sigo árvores de comportamento, planejamento de trajetória e controle de referências de velocidade linear e angular. Por fim, são apresentados os desafios de se trabalhar com sistemas deste tipo e o desempenho da solução desenvolvida em ambiente de simulação. Para tanto, foram realizados o mapeamento do ambiente, uma simulação de processo de transporte de carga e uma demonstração da capacidade do sistema de prevenir colisão com obstáculos.Applications involving cargo and people transport in controlled industrial environments are part of several contexts found on the market. Currently, in these applications, a transition moment is observed, from the use of human labor or limited mobile robots to the use of robots capable of adaptation in dynamic environments. The goal of this work is to present a navigation system solution for self-driving vehicles capable of trav- eling in open industrial environments, aiming at cargo transport applications. During development, it was intended to base the solution’s architecture in real analog systems. Therefore, a research stage was carried out at the beginning of the project. The final solution is presented in parts, with a localization subsystem and a decision making subsystem. In the localization subsystem, odometry, SLAM and AMCL techniques are used for building the information blocks necessary for the decision making subsystem operation. The decision making subsystem is composed by behavior trees, path plan- ning and control of linear and angular velocity references. Finally, the development challenges are presented along with the analysis of performance for the solution in a simulated environment. For this purpose, we carried a mapping process, a cargo transport process simulation and a collision avoidance demonstration

Physics-Informed Neural Nets for Control of Dynamical Systems

Physics-informed neural networks (PINNs) impose known physical laws into the learning of deep neural networks, making sure they respect the physics of the process while decreasing the demand of labeled data. For systems represented by Ordinary Differential Equations (ODEs), the conventional PINN has a continuous time input variable and outputs the solution of the corresponding ODE. In their original form, PINNs do not allow control inputs neither can they simulate for long-range intervals without serious degradation in their predictions. In this context, this work presents a new framework called Physics-Informed Neural Nets for Control (PINC), which proposes a novel PINN-based architecture that is amenable to \emph{control} problems and able to simulate for longer-range time horizons that are not fixed beforehand. The framework has new inputs to account for the initial state of the system and the control action. In PINC, the response over the complete time horizon is split such that each smaller interval constitutes a solution of the ODE conditioned on the fixed values of initial state and control action for that interval. The whole response is formed by feeding back the predictions of the terminal state as the initial state for the next interval. This proposal enables the optimal control of dynamic systems, integrating a priori knowledge from experts and data collected from plants into control applications. We showcase our proposal in the control of two nonlinear dynamic systems: the Van der Pol oscillator and the four-tank system

Severe Weather Events over Southeastern Brazil during the 2016 Dry Season

Southeastern Brazil is the most populated and economically developed region of this country. Its climate consists of two distinct seasons: the dry season, extending from April to September, the precipitation is significantly reduced in comparison to that of the wet season, which extends from October to March. However, during nine days of the 2016 dry season, successive convective systems were associated with atypical precipitation events, tornadoes and at least one microburst over the southern part of this region. These events led to flooding, damages to buildings, shortages of electricity and water in several places, many injuries, and two documented deaths. The present study investigates the synoptic and dynamical features related to these anomalous events. The convective systems were embedded in an unstable environment with intense low-level jet flow and strong wind shear and were supported by a sequence of extratropical cyclones occurring over the Southwest Atlantic Ocean. These features were intensified by the Madden–Julian oscillation (MJO) in its phase 8 and by intense negative values of the Pacific South America (PSA) 2 mode