Abstract
Flocking algorithms allow high level organization in huge groups of agents. We deal with a multitarget variation of flocking. In this variation, each agent chooses a target to follow, and several flocks are formed then. One important disadvantage of the previously proposed multitarget flocking models is that they assume that agents move in an environment without restrictions. That is, there are no objects that constrain the mobility of agents, such as obstacles. This drawback limits potential applications of multitarget flocking models such as multirobot systems and unmanned aerial vehicles. In this work, we proposed a stable multitarget algorithm based on Particle Swarm Optimization to solve the problem mentioned above. System behavior was rigorously measured to conclude that our proposal models multitarget flocking in constrained environments.
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Barrera, A.S., López-López, A., Gómez, G.R. (2012). Multitarget Flocking for Constrained Environments. In: Demazeau, Y., Müller, J., Rodríguez, J., Pérez, J. (eds) Advances on Practical Applications of Agents and Multi-Agent Systems. Advances in Intelligent and Soft Computing, vol 155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28786-2_20
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DOI: https://doi.org/10.1007/978-3-642-28786-2_20
Publisher Name: Springer, Berlin, Heidelberg
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