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Soft Computing for Swarm Robotics: New Trends and Applications

Abstract: Robotics have experienced a meteoric growth over the last decades, reaching unprecedented levels of distributed intelligence and self-autonomy. Today, a myriad of real-world scenarios can benefit from the application of robots, such as structural health monitoring, complex manufacturing, efficient logistics or disaster management. Related to this topic, there is a paradigm connected to Swarm Intelligence which is grasping significant interest from the Computational Intelligence community. This branch of knowledge is known as Swarm Robotics, which refers to the development of tools and techniques to ease the coordination of multiple small-sized robots towards the accomplishment of difficult tasks or missions in a collaborative fashion. The success of Swarm Robotics applications comes from the efficient use of smart sensing, communication and organization functionalities endowed to these small robots, which allow for collaborative information sensing, operation and knowledge inference from the environment. The numerous industrial and social applications that can be addressed efficiently by virtue of swarm robotics unleashes a vibrant research area focused on distributing intelligence among autonomous agents with simple behavioral rules and communication schedules, yet potentially capable of realizing the most complex tasks. In this context, we present and overview recent contributions reported around this paradigm, which serves as an exemplary excerpt of the potential of Swarm Robotics to become a major research catalyst of the Computational Intelligence arena in years to come.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Fuente: Journal of Computational Science Volume 39, January 2020, 101049

Editorial: Elsevier

 Fecha de publicación: 01/01/2020

Nº de páginas: 4

Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.jocs.2019.101049

ISSN: 1877-7503,1877-7511

Proyecto español: TIN2017-89275-R

Url de la publicación: https://doi.org/10.1016/j.jocs.2019.101049