This paper presents an updated and broad review of swarm robotics research papers regarding software, hardware, simulators and applications. The evolution from its concept to its real-life implementation is presented. Swarm robotics analysis is focused on four aspects: conceptualization, simulators, real-life robotics for swarm use, and applications. For simulators and robots, a detailed comparison between existing resources is made. A summary of the most used swarm robotics applications and behaviors is included.

The coordination of a swarm of simulated robots is proposed as a method for finding paths to a specific target in unknown environments. The solution to creating the most viable pathway to a desired target can be derived through swarm robotics by deploying a bio-inspired exploration algorithm based in a cellular automata model. This algorithm uses virtual pheromones to execute a better dispersion of the agents through the environment in order to decrease the iterations need it to cover it. This model is compared with the classic Random Walk algorithm, which works in a probabilistic and random way. Using the environment map, once obstacles have been identified, an adapted Rapidly-exploring Random Graph (RRG) scheme is developed to structure the environment through a network. From there a Dijkstra path planning algorithm is applied with the aim of defining an optimized route to a specific target. Application of that procedure creates an efficient and effective scheme for finding a path into unknown environments. For this study, performance simulations have been included to refine parameters in order to compare results with different strategies.

In this paper, we present a scheme to solve the problem to explore, cover and define paths in dangerous environments. The proposal is organized in three phases: exploration, mapping and path planning. Swarm robotics is used in the first phase along with a stigmergy approach for the communication between the agents. The explored cell will be stored in order to create a grid map. In the path planning phase, a multiobjective problem is used to minimize distance and danger, through a graph obtained by an adapted RRG, and a genetic algorithm called modified NSGA-II.

Se presenta un método alternativo para determinar condiciones de operación anómalas en un motor trifásico de inducción, basado en un conjunto de índices adimensionales, calculados a partir de sus pérdidas de potencia. Estos índices son sensibles a variaciones en las condiciones en que opera el motor y por lo tanto se valorará si, en conjunto, son capaces de discriminar entre un desbalance de voltaje, un subvoltaje o una variación térmica, entre otros. En este trabajo se desarrollan las ecuaciones necesarias para calcular las perdidas eléctricas que permiten el cálculo de los índices propuestos. Es importante destacar que estos índices unifican la interpretación de variables eléctricas, mecánicas y térmicas con la intensión de su utilización en el diseño de un sistema automático de detección de faltas que opere en tiempo real, por medio de un prototipo basado en redes neuronales. Adicionalmente, se describen los experimentos realizados a un motor trifásico de inducción de dos caballos de fuerza, 230 Voltios - 60 Hertz, el banco de pruebas construido y en la parte final del trabajo se representa de forma gráfica la variabilidad de los índices ante distintas condiciones anormales, lo que permite valorar su sensibilidad y relación con las faltas asociadas.