31 research outputs found
Safe navigation and human-robot interaction in assistant robotic applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots
The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so
Swarm Robotics
Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties
XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas
Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI
XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas
Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI
Novel approaches to cooperative coevolution of heterogeneous multiagent systems
Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2017Heterogeneous multirobot systems are characterised by the morphological and/or behavioural heterogeneity of their constituent robots. These systems have a number of advantages over the more common homogeneous multirobot systems: they can leverage specialisation for increased efficiency, and they can solve tasks that are beyond the reach of any single type of robot, by combining the capabilities of different robots. Manually designing control for heterogeneous systems is a challenging endeavour, since the desired system behaviour has to be decomposed into behavioural rules for the individual robots, in such a way that the team as a whole cooperates and takes advantage of specialisation. Evolutionary robotics is a promising alternative that can be used to automate the synthesis of controllers for multirobot systems, but so far, research in the field has been mostly focused on homogeneous systems, such as swarm robotics systems. Cooperative coevolutionary algorithms (CCEAs) are a type of evolutionary algorithm that facilitate the evolution of control for heterogeneous systems, by working over a decomposition of the problem. In a typical CCEA application, each agent evolves in a separate population, with the evaluation of each agent depending on the cooperation with agents from the other coevolving populations. A CCEA is thus capable of projecting the large search space into multiple smaller, and more manageable, search spaces. Unfortunately, the use of cooperative coevolutionary algorithms is associated with a number of challenges. Previous works have shown that CCEAs are not necessarily attracted to the global optimum, but often converge to mediocre stable states; they can be inefficient when applied to large teams; and they have not yet been demonstrated in real robotic systems, nor in morphologically heterogeneous multirobot systems. In this thesis, we propose novel methods for overcoming the fundamental challenges in cooperative coevolutionary algorithms mentioned above, and study them in multirobot domains: we propose novelty-driven cooperative coevolution, in which premature convergence is avoided by encouraging behavioural novelty; and we propose Hyb-CCEA, an extension of CCEAs that places the team heterogeneity under evolutionary control, significantly improving its scalability with respect to the team size. These two approaches have in common that they take into account the exploration of the behaviour space by the evolutionary process. Besides relying on the fitness function for the evaluation of the candidate solutions, the evolutionary process analyses the behaviour of the evolving agents to improve the effectiveness of the evolutionary search. The ultimate goal of our research is to achieve general methods that can effectively synthesise controllers for heterogeneous multirobot systems, and therefore help to realise the full potential of this type of systems. To this end, we demonstrate the proposed approaches in a variety of multirobot domains used in previous works, and we study the application of CCEAs to new robotics domains, including a morphological heterogeneous system and a real robotic system.Fundação para a Ciência e a Tecnologia (FCT, PEst-OE/EEI/LA0008/2011
Optimización en sistemas multi-robot mediante embodied evolution
Programa Oficial de Doutoramento en Computación. 5009V01[Resumen]
En esta tesis se ha desarrollado una versión del algoritmo evolutivo Embodied
Evolution (EE) que generaliza las existentes en el campo, con el objetivo
de avanzar en la estandarización de este paradigma de forma que pueda ser
estudiado de manera formal, y así conocer sus fortalezas y limitaciones. El algoritmo
desarrollado en esta tesis se ha denominado “canónico”porque se ha
diseñado tras el análisis detallado de los procesos básicos comunes a las diferentes
variantes dentro de Embodied Evolution, de modo que únicamente contiene
dichos procesos intrínsecos, y una parametrización de los mismos lo más general
posible. El funcionamiento de este algoritmo can´onico de Embodied Evolution
se ha analizado en un conjunto de funciones teóricas representativas de los espacios
de búsqueda en optimización colectiva, sobre los que se ha llevado a cabo
un análisis de sensibilidad exhaustivo. Finalmente, las conclusiones del análisis
teórico se han validado en una tarea real en la cual se ha podido comprobar la
validez de la aproximación a la hora de optimizar la coordinación emergente del
sistema multi-robot, tanto a nivel de rendimiento como a nivel de organización
automática en especies, una propiedad fundamental de este paradigma.[Resumo] Nesta tese doutoral desenvolveuse unha version do algoritmo evolutivo Embodied
Evolution (EE) que xeneraliza as existentes no campo, co obxectivo de
avanzar na estandarización deste paradigma, de forma que poida ser estudado
de maneira formal, e así coñecer as súas fortalezas e limitacións. O algoritmo
desenvolvido nesta tese denominouse algoritmo canónico porque foi deseñado
tras analizar detalladamente os procesos básicos comúns ás diferentes variantes
dentro de Embodied Evolution, de modo que únicamente contén estes procesos
intrínsecos, e una parametrización dos mesmos o máis xeral posible. O funcionamiento
do algoritmo canónico de Embodied Evolution analizouse nun conxunto
de funcións teóricas representativas dos espazos de búsqueda en optimización
colectiva, sobre os que se realizou unha análise de sensibilidade exhaustiva. Finalmente,
as conclusions da análise teorica validouse nunha tarefa real, na que
se puido comprobar a validez da aproximación á hora de optimizar a coordinación emerxente do sistema multi-robot, tanto a nivel de rendemento como
a nivel de organización automática en especies, unha propiedade fundamental
neste paradigma.[Abstract] In this PhD thesis, a version of the Embodied Evolution (EE) algorithm
has been developed that generalizes the existing version on the field, with the
goal of advancing in the standardization of this paradigm such that it could
be studied in a formal way and discover its strengths and limitations. The developed
algorithm has been named “canonical” because it was designed after
analyzing in detail the basic common processes of the di↵erent variants in Embodied
Evolution, in a way that only contains the intrinsic processes and a
parametrization as general as possible. The operation of this Embodied Evolution
canonical algorithm has been analyzed in a set of theoretic functions that
represent the di↵erent search landscapes in collective optimization, in which a
sensibility analysis has been performed. Finally, the conclusions of the theoretic
analysis has been validated in a real task in which the validity of the approximation
has been confirmed through the successful optimization of the emergent
coordination of the multi-robot system, both in performance and in automatic
organization in species, a fundamental property in this paradigm