Prototipação e verificação formal de sistema autônomo com propriedades tempo-real : um estudo de caso no Body Sensor Network

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Curso de Graduação em Engenharia de Controle e Automação, 2017.O desenvolvimento de dispositivos micro eletro-mecânicos em paralelo ao avanço da capacidade de processamento e comunicação em rede traz novas possibilidades de imersão de tecnologias no diaa-dia do ser humano. Na perspectiva da engenharia, essas possibilidades alavancam novos desafios em cenários que demandam resposta em tempo real, em particular projeto e desenvolvimento de sistemas autônomos com garantias de dependabilidade a nível de software. Neste trabalho, foi implementada uma aplicação do Body Sensor Network (BSN), um protótipo de rede de sensores para monitoramento de sinais vitais do corpo humano com o objetivo de detectar situações emergenciais e, a partir da metodologia de controle para automação proposta no artigo seminal "Software Engineering meets Control Theory" com utilização da ferramenta UPPAAL para modelagem e validação, um controlador foi desenvolvido e implementado no protótipo com garantias de propriedades de tempo real. Por fim, o protótipo autônomo é avaliado com o intuito de levantar contribuições da aplicação de verificação de modelos formais no projeto de sistemas autônomos com resposta em tempo real.The development of microelectro-mechanical devices in parallel with the advance of the capacity of processing and wireless communication brings new possibilities of immersion of pervasive technologies in human daily activities. From the perspective of engineering, these possibilities lead to new challenges in scenarios that demand real-time response, particularly design and development of autonomous systems with software-level dependability guarantees. In this work, an application of the Body Sensor Network (BSN), a sensor network prototype for vital signs monitoring was implemented, aiming emergency detection and, based on the control methodology for automation proposed in the seminal article "Software Engineering meets Control Theory"with UPPAAL tool for modeling and validation, a controller was developed and implemented in the prototype with guarantees of real-time properties. Finally, the autonomous prototype is evaluated with the intention of raising contributions from the use of formal verification in the design of autonomous systems with real-time response

Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems

A hybrid approach combining control theory and AI for engineering self-adaptive systems

Control theoretical techniques have been successfully adopted as methods for self-adaptive systems design to provide formal guarantees about the effectiveness and robustness of adaptation mechanisms. However, the computational effort to obtain guarantees poses severe constraints when it comes to dynamic adaptation. In order to solve these limitations, in this paper, we propose a hybrid approach combining software engineering, control theory, and AI to design for software self-adaptation. Our solution proposes a hierarchical and dynamic system manager with performance tuning. Due to the gap between high-level requirements specification and the internal knob behavior of the managed system, a hierarchically composed components architecture seek the separation of concerns towards a dynamic solution. Therefore, a two-layered adaptive manager was designed to satisfy the software requirements with parameters optimization through regression analysis and evolutionary meta-heuristic. The optimization relies on the collection and processing of performance, effectiveness, and robustness metrics w.r.t control theoretical metrics at the offline and online stages. We evaluate our work with a prototype of the Body Sensor Network (BSN) in the healthcare domain, which is largely used as a demonstrator by the community. The BSN was implemented under the Robot Operating System (ROS) architecture, and concerns about the system dependability are taken as adaptation goals. Our results reinforce the necessity of performing well on such a safety-critical domain and contribute with substantial evidence on how hybrid approaches that combine control and AI-based techniques for engineering self-adaptive systems can provide effective adaptation

Regulation of immunity during visceral Leishmania infection

Unicellular eukaryotes of the genus Leishmania are collectively responsible for a heterogeneous group of diseases known as leishmaniasis. The visceral form of leishmaniasis, caused by L. donovani or L. infantum, is a devastating condition, claiming 20,000 to 40,000 lives annually, with particular incidence in some of the poorest regions of the world. Immunity to Leishmania depends on the development of protective type I immune responses capable of activating infected phagocytes to kill intracellular amastigotes. However, despite the induction of protective responses, disease progresses due to a multitude of factors that impede an optimal response. These include the action of suppressive cytokines, exhaustion of specific T cells, loss of lymphoid tissue architecture and a defective humoral response. We will review how these responses are orchestrated during the course of infection, including both early and chronic stages, focusing on the spleen and the liver, which are the main target organs of visceral Leishmania in the host. A comprehensive understanding of the immune events that occur during visceral Leishmania infection is crucial for the implementation of immunotherapeutic approaches that complement the current anti-Leishmania chemotherapy and the development of effective vaccines to prevent disease.The research leading to these results has received funding from the European Community’s Seventh Framework Programme under grant agreement No.602773 (Project KINDRED). VR is supported by a post-doctoral fellowship granted by the KINDReD consortium. RS thanks the Foundation for Science and Technology (FCT) for an Investigator Grant (IF/00021/2014). This work was supported by grants to JE from ANR (LEISH-APO, France), Partenariat Hubert Curien (PHC) (program Volubilis, MA/11/262). JE acknowledges the support of the Canada Research Chair Program

Trajetórias da Educomunicação nas Políticas Públicas e a Formação de seus Profissionais

Esta obra é composta com os trabalhos apresentados no primeiro subtema, TRAJETÓRIA – Educação para a Comunicação como Política pública, nas perspectivas da Educomunicação e da Mídia-Educação, do II Congresso Internacional de Comunicação e Educação. Os artigos pretendem propiciar trocas de informações e produzir reflexões com os leitores sobre os caminhos percorridos, e ainda a percorrer, tendo como meta a expansão e a legitimação das práticas educomunicativas e/ou mídia-educativas como política pública para o atendimento à formação de crianças, adolescentes, jovens e adultos, no Brasil e no mundo

Engineering Software for Resilient Cyber-Physical Systems

Designing, implementing, and verifying resilient cyber-physical systems is challenging. Resilience is the ability to provide the required capability when facing adversity. Resilient cyber-physical systems should avoid, withstand, recover from, and evolve and adapt to cope with adversity stemming from computation, networking, or physical environment. From the engineering point of view, the usefulness of such systems is hindered by their lack of ability to adapt and overcome unknown stimuli, ever-changing and conflicting objectives, and deprecated internal components. Software as a tool for self-management is a key instrument for dealing with uncertainty. Yet, engineering software for resilient cyber-physical systems is hard since the effects of operating under the unknown might emerge during the execution, requesting decision-making at runtime rather than design time. Decision-making at runtime should guarantee the satisfaction of system goals, work efficiently to be effectively used in practice, and guarantee the expected quality.With this in mind, this thesis contributes towards the engineering of software for resilient cyber-physical systems by (i) combining control theory and artificial intelligence for efficient adaptation, (ii) using formal methods for ensuring correctness of control-theoretic software adaptation, and (iii) promoting a language for scenario-based testing autonomous systems. We found that the hybrid approach, combining control theory and artificial intelligence, improves the efficiency of the adaptation mechanism. The results shed light on the interplay between control theory and artificial intelligence as fundaments for engineering resilient cyber-physical systems. Yet, incorporating machine learning and control theory introduces non-deterministic autonomic behavior, posing a challenge for the assurance provision for such tools. On the one hand, we found that the use of formal methods helps to build confidence in software-based controllers. On the other hand, large and complex systems place barriers to the usage of formal methods. Thus, we explore the use of testing and specifically scenario-based testing for validating large and complex cyber-physical systems that are required to operate in complex and unpredictable environments, like autonomous vehicles. In a nutshell, this thesis argues in favor of introducing control theory and artificial intelligence in designing and implementing software-based controllers. Also, we exploit formal methods and testing as instruments for verifying and validating cyber-physical systems

EzSkiROS: A Case Study on Embedded Robotics DSLs to Catch Bugs Early

When we develop general-purpose robot software components, we rarely know the full context that they will execute in. This limits our ability to make predictions, including our ability to detect program bugs early. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loop or even cause safety hazards. In this paper, we propose an approach to help developers find bugs early with minimal additional effort by using embedded Domain-Specific Languages (DSLs) that enforce early checks. We describe DSL design patterns suitable for the robotics domain and demonstrate our approach for DSL embedding in Python, using a case study on an industrial tool SkiROS2, designed for the composition of robot skills. We demonstrate our patterns on the embedded DSL EzSkiROS and show that our approach is effective in performing safety checks while deploying code on the robot, much earlier than at runtime. An initial study with SkiROS2 developers show that our DSL-based approach is useful for early bug detection and improving the maintainability of robot code

Towards Mapping Control Theory and Software Engineering Properties using Specification Patterns

A traditional approach to realize self-adaptation in software engineering (SE) is by means of feedback loops. The goals of the system can be specified as formal properties that are verified against models of the system. On the other hand, control theory (CT) provides a well-established foundation for designing feedback loop systems and providing guarantees for essential properties, such as stability, settling time, and steady state error. Currently, it is an open question whether and how traditional SE approaches to self-adaptation consider properties from CT. Answering this question is challenging given the principle differences in representing properties in both fields. In this paper, we take a first step to answer this question. We follow a bottom up approach where we specify a control design (in Simulink) for a case inspired by Scuderia Ferrari (F1) and provide evidence for stability and safety. The design is then transferred into code (in C) that is further optimized. Next, we define properties that enable verifying whether the control properties still hold at code level. Then, we consolidate the solution by mapping the properties in both worlds using specification patterns as common language and we verify the correctness of this mapping. The mapping offers a reusable artifact to solve similar problems. Finally, we outline opportunities for future work, particularly to refine and extend the mapping and investigate how it can improve the engineering of self-adaptive systems for both SE and CT engineers

An architecture for mission coordination of heterogeneous robots

Context: Robots can potentially collaborate to execute a variety of tasks in the service robots domain. However, developing applications of service robots can be complex due to the high level of uncertainty and required level of autonomy. Objective: We aim at contributing an architecture for the development of applications, capable of coordinating multi-robot missions, and that promotes modifiability and seamless integration of independently developed components. Method: In this work, we introduce MissionControl: an ensemble-based architecture to coordinate missions of heterogeneous robots to autonomously form coalitions. MissionControl comprises a component model and a runtime environment. The component model specifies how the system can be extended for different robot\u27s behaviors and environments. The runtime environment provides the processes required for coordinating the execution of missions at runtime. Results: We evaluated MissionControl in a simulated environment in the healthcare domain. We randomly generated 81 scenarios with uncertainty in the robots’ initial configurations. Then, each scenario was executed 8 times (i.e. 648 runs), where we evaluated the feasibility and efficiency of MissionControl for autonomously forming coalitions against a baseline approach that uses a random robot allocation. Statistical hypotheses testing yielded that MissionControl was able to achieve higher success rates while reducing the required time to conclude a mission, when compared to a baseline approach. We also perform an evaluation of the key quality attributes of the architecture, i.e. modifiability and integrability. Conclusions: MissionControl demonstrated itself able to coordinate multi-robot missions by autonomously assigning missions. Despite the error-prone robotic mission environment and demanding computational resources, MissionControl led to a significant increase in the success rate, while also decreasing the time required to conclude robotic missions when compared to a baseline approach