![]() ![]() HAFLoop, in conjunction with its implementation in the form of a framework named HAFLoop4J, is a generic and reusable solution that easies the design and development of adaptive feedback loops, from higher to lower levels. Although great efforts have been done for supporting the adaptation of feedback loops in SASs’, none of the state-of-the-art solutions satisfactorily addresses all the open research challenges. We have also studied how state-of-the-art approaches support adaptive monitoring in current monitoring systems. ![]() We have identified open research challenges affecting SASs’ and feedback loops’ adaptation and analyzed whether and how existing approaches address those challenges. Concretely, we have presented HAFLoop, an architectural proposal for supporting the adaptation of the MAPE-K loop at runtime. Motivated by this fact, in this thesis, we address the automatic runtime adaptation of SASs’ feedback control loops, particularly the Monitor element, in order to respond to changes in the systems, the environment and the elements of the loop themselves. If that the case, current self-adaptive systems would not be able to react to unpredictable runtime events such as faults or changing requirements. Assuming static feedback loops implies that the structure and behavior of the elements of the loop should be determined at design-time and cannot change at runtime, i.e., in the case of the Monitor, systems’ owners should know everything to be monitored at design time. ![]() In this loop, the Monitor element plays a crucial role since the quality of the monitoring data (e.g., timeliness, freshness, accuracy, availability, etc.) affects directly the performance of the rest of the elements of the loop, and in consequence the quality of the resulting adaptation decisions. One of the most popular feedback loops is the MAPE-K loop. Wolovich, W.: Robotics: Basic Analysis and Design.Nowadays, most of the approaches supporting self-adaptive systems (SASs) rely on static feedback control loops, for managing their adaptation process. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, vol. 1(1) (2009)Įlfes, A.: Using occupancy grids for mobile robot perception and navigation. Mondada, F., et al.: The e-puck, a robot designed for education in engineering. Official Python Programming Language Website, Zelle, J.: Python Programming: An Introduction to Computer Science. Siciliano, B., Khatib, O.: Springer Handbook of Robotics. In: Exploring Artificial Intelligence in the new Millennium, pp. ![]() Technical Report, The City College of New York of the City University of New York IEEE Robotics and Automation Magazine 2 (2006)īennet, S., Nieto-Wire, C., Peche, J., Timotheu, M., Vasili-Acevedo, D.: Mobile Robot Simultaneous Localization and Mapping in Static Environments. MIT, Massachusetts (2004)īailey, T., Durrant-Whyte, H.: Simultaneous Localisation and Mapping (SLAM): Part I The Essential Algorithms. Siegwart, R., Nourbakhsh, I.: Introduction to Autonomous Mobile Robots. Vaughan, R.: Massively Multiple Robot Simulations in Stage. In: Proceedings of the 11th International Conference on Advanced Robotics (ICAR 2003), Coimbra, Portugal, pp. Vaughan, R., Howard, A., Gerkey, B.: The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems. ![]()
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