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Swarm-like Methodologies for Executing Tasks with Deadlines

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Abstract

Very few studies have been carried out to test multi-robot task allocation swarm algorithms in real time systems, where each task must be executed before a deadline. This paper presents a comparative study of several swarm-like algorithms and auction based methods for this kind of scenarios. Moreover, a new paradigm called pseudo-probabilistic swarm-like, is proposed, which merges characteristics of deterministic and probabilistic classical swarm approaches. Despite that this new paradigm can not be classified as swarming, it is closely related with swarm methods. Pseudo-probabilistic swarm-like algorithms can reduce the interference between robots and are particularly suitable for real time environments. This work presents two pseudo-probabilistic swarm-like algorithms: distance pseudo-probabilistic and robot pseudo-probabilistic. The experimental results show that the pseudo-probabilistic swarm-like methods significantly improve the number of finished tasks before a deadline, compared to classical swarm algorithms. Furthermore, a very simple but effective learning algorithm has been implemented to fit the parameters of these new methods. To verify the results a foraging task has been used under different configurations.

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  1. Departament de Matemàtiques i Informàtica, Universitat de les Illes Baleares, Cra. de Valldemossa, Km. 7.5, 07122, Palma, (Balears), Spain

    José Guerrero & Gabriel Oliver

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  1. José Guerrero
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  2. Gabriel Oliver
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Correspondence to José Guerrero.

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This work has been partially supported by project DPI2008-06548-C03-02 and FEDER funding.

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Guerrero, J., Oliver, G. Swarm-like Methodologies for Executing Tasks with Deadlines. J Intell Robot Syst 68, 3–19 (2012). https://doi.org/10.1007/s10846-012-9666-9

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