Title

Multirobot Behavior Synchronization Through Direct Neural Network Communication

Keywords

Artificial neural networks; Communication; Coordination; Evolutionary algorithms; HyperNEAT; Multirobot teams

Abstract

Many important real-world problems, such as patrol or search and rescue, could benefit from the ability to train teams of robots to coordinate. One major challenge to achieving such coordination is determining the best way for robots on such teams to communicate with each other. Typical approaches employ hand-designed communication schemes that often require significant effort to engineer. In contrast, this paper presents a new communication scheme called the hive brain, in which the neural network controller of each robot is directly connected to internal nodes of other robots and the weights of these connections are evolved. In this way, the robots can evolve their own internal "language" to speak directly brain-to-brain. This approach is tested in a multirobot patrol synchronization domain where it produces robot controllers that synchronize through communication alone in both simulation and real robots, and that are robust to perturbation and changes in team size. © Springer-Verlag Berlin Heidelberg 2012.

Publication Date

12-1-2012

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

7507 LNAI

Issue

PART 2

Number of Pages

603-614

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-642-33515-0_59

Socpus ID

84892875269 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84892875269

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