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Article contents

Understanding the communication complexity of the robotic Darwinian PSO

Published online by Cambridge University Press:  13 February 2014

Micael S. Couceiro*
Affiliation:
Institute of Systems and Robotics (ISR), Department of Electrical and Computer Engineering, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal RoboCorp, Engineering Institute of Coimbra, Quinta da Nora, 3030-199 Coimbra, Portugal
Amadeu Fernandes
Affiliation:
Institute of Systems and Robotics (ISR), Department of Electrical and Computer Engineering, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal
Rui P. Rocha
Affiliation:
Institute of Systems and Robotics (ISR), Department of Electrical and Computer Engineering, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal
Nuno M. F. Ferreira
Affiliation:
RoboCorp, Engineering Institute of Coimbra, Quinta da Nora, 3030-199 Coimbra, Portugal
*
*Corresponding author. E-mail: micaelcouceiro@gmail.com

Summary

An extension of the well-known Particle Swarm Optimization (PSO) to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefited from the dynamical partitioning of the whole population of robots. Although such strategy allows decreasing the amount of required information exchange among robots, a further analysis on the communication complexity of the RDPSO needs to be carried out so as to evaluate the scalability of the algorithm. Moreover, a further study on the most adequate multi-hop routing protocol should be conducted. Therefore, this paper starts by analyzing the architecture and characteristics of the RDPSO communication system, thus describing the dynamics of the communication data packet structure shared between teammates. Such procedure will be the first step to achieving a more scalable implementation of RDPSO by optimizing the communication procedure between robots. Second, an ad hoc on-demand distance vector reactive routing protocol is extended based on the RDPSO concepts, so as to reduce the communication overhead within swarms of robots. Experimental results with teams of 15 real robots and 60 simulated robots show that the proposed methodology significantly reduces the communication overhead, thus improving the scalability and applicability of the RDPSO algorithm.

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Articles
Copyright
Copyright © Cambridge University Press 2014 

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