Osaba, EnekoDel Ser, JavierJubeto, XabierIglesias, AndrésFister, IztokGálvez, AkemiAnalide, CesarNovais, PauloCamacho, DavidYin, Hujun2020-10-27Osaba , E , Del Ser , J , Jubeto , X , Iglesias , A , Fister , I & Gálvez , A 2020 , Distributed Coordination of Heterogeneous Robotic Swarms Using Stochastic Diffusion Search . in C Analide , P Novais , D Camacho & H Yin (eds) , unknown . vol. 12490 , 0302-9743 , Springer , pp. 79-91 , 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 , Guimaraes , Portugal , 4/11/20 . https://doi.org/10.1007/978-3-030-62365-4_8conference978-3-030-62365-4; 978-3-030-62364-79783030623647researchoutputwizard: 11556/1033Publisher Copyright: © 2020, Springer Nature Switzerland AG.The term Swarm Robotics collectively refers to a population of robotic devices that efficiently undertakes diverse tasks in a collaborative way by virtue of computational intelligence techniques. This paradigm has given rise to a profitable stream of contributions in recent years, all sharing a clear consensus on the performance benefits derived from the increased exploration capabilities offered by Swarm Robotics. This manuscript falls within this topic: specifically, it gravitates on an heterogeneous Swarm Robotics system that relies on Stochastic Diffusion Search (SDS) as the coordination heuristics for the exploration, location and delimitation of areas scattered over the area in which robots are deployed. The swarm is composed by agents of diverse kind, which can be ground robots or flying devices. These agents communicate to each other and cooperate towards the accomplishment of the exploration tasks comprising the mission of the overall swarm. Furthermore, maps contain several obstacles and dangers, implying that in order to enter a specific area, robots should meet certain conditions. Experiments are conducted over three different maps and three implemented solving approaches. Conclusions are drawn from the obtained results, confirming that i) SDS allows for a lightweight, heuristic mechanism for the coordination of the robots; and ii) the most efficient swarming approach is the one comprising a heterogeneity of ground and aerial robots.132080805enginfo:eu-repo/semantics/restrictedAccessconference output10.1007/978-3-030-62365-4_8Swarm RoboticsStochastic Diffusion SearchSwarm IntelligenceUnmanned Aerial VehiclesRoboticsSwarm RoboticsStochastic Diffusion SearchSwarm IntelligenceUnmanned Aerial VehiclesRoboticsTheoretical Computer ScienceGeneral Computer ScienceFunding InfoBasque Government for its support through the EMAITEK and ELKARTEK (Elkarbot project) programs. Javier Del Ser also receives support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of this institution.Basque Government for its support through the EMAITEK and ELKARTEK (Elkarbot project) programs. Javier Del Ser also receives support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of this institution.http://www.scopus.com/inward/record.url?scp=85097208461&partnerID=8YFLogxK