Recent miniaturization in Unmanned Aerial Vehicles (UAVs) technology encourages the use of many small UAVs for search missions in unknown environments, provided that the autonomous and adaptive coordination logic can be e ective. In this research field, biologically inspired metaheuristics have been proposed to mimics swarms, flocks, and other coordination schemas. The design and management of such systems is a research challenge when considering (i) combination and optimization of multiple metaheuristics and (ii) enhancements of biologically inspired metaheuristic through technological advances. In this paper the swarm coordination of UAVs employed in target search is based on ocking and stigmergy, to provide robust formation control and dynamic environmental information sharing, respectively. The design of both metaheuristics takes into account UAVs equipment, and the coordination logic is adapted to the mission by means of a di erential evolutionary algorithm. This algorithm optimizes the aggregated structural parameters of all metaheuristics to allow the most efficient coordination with respect to the mission environment. Some possible enhancements of stigmergy are studied by simulating target search tasks on synthetic and real-world scenarios.
Enhancing biologically inspired swarm behavior: metaheuristics to foster the optimization of UAVs coordination in target search
Antonio L. Alfeo
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2019-01-01
Abstract
Recent miniaturization in Unmanned Aerial Vehicles (UAVs) technology encourages the use of many small UAVs for search missions in unknown environments, provided that the autonomous and adaptive coordination logic can be e ective. In this research field, biologically inspired metaheuristics have been proposed to mimics swarms, flocks, and other coordination schemas. The design and management of such systems is a research challenge when considering (i) combination and optimization of multiple metaheuristics and (ii) enhancements of biologically inspired metaheuristic through technological advances. In this paper the swarm coordination of UAVs employed in target search is based on ocking and stigmergy, to provide robust formation control and dynamic environmental information sharing, respectively. The design of both metaheuristics takes into account UAVs equipment, and the coordination logic is adapted to the mission by means of a di erential evolutionary algorithm. This algorithm optimizes the aggregated structural parameters of all metaheuristics to allow the most efficient coordination with respect to the mission environment. Some possible enhancements of stigmergy are studied by simulating target search tasks on synthetic and real-world scenarios.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
