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Abstract

Chapter 10 illustrated mapping with a team of identical robots. Chapter 11 will go beyond mapping and discuss capabilities and limitations encountered with multiple robots. For a number of years, the U.S. Department of Defense promoted research about creating robots that behaved in formation (Balch and Arkin, 1998). More recently, heterogeneous robots were considered as teams. Balch and Parker (2002) suggested correlating heterogeneity with performance. Hierarchical social entropy was developed as an application of Shannon’s information entropy metric to robotic groups that provides a continuous, quantitative measure of robot team diversity (Shannon and Weaver, 1971). The metric captures important components of the meaning of diversity, including the number and size of behavioral groups in a society, and the extent to which agents differ. The utility of the metrics is demonstrated in the experimental evaluation of multi-robot soccer and multi-robot foraging teams.

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Hexmoor, H. (2013). Multi-Robotics Phenomena. In: Essential Principles for Autonomous Robotics. Synthesis Lectures on Artificial Intelligence and Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-031-01563-2_11

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