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Optimizing multi-robot communication under bandwidth constraints

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Abstract

Robots working collaboratively can share observations with others to improve team performance, but communication bandwidth is limited. Recognizing this, an agent must decide which observations to communicate to best serve the team. Accurately estimating the value of a single communication is expensive; finding an optimal combination of observations to put in the message is intractable. In this paper, we present OCBC, an algorithm for Optimizing Communication under Bandwidth Constraints. OCBC uses forward simulation to evaluate communications and applies a bandit-based combinatorial optimization algorithm to select what to include in a message. We evaluate OCBC’s performance in a simulated multi-robot navigation task. We show that OCBC achieves better task performance than a state-of-the-art method while communicating up to an order of magnitude less.

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Acknowledgements

This material is based upon work supported by the National Science Foundation (NSF) Graduate Research Fellowship Program under Grant No. DGE 1256260, as well as NSF Grant Nos. CCF 1442773 and NRI 1830615. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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  1. University of Michigan, Ann Arbor, MI, USA

    Ryan J. Marcotte, Xipeng Wang, Dhanvin Mehta & Edwin Olson

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  1. Ryan J. Marcotte
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  2. Xipeng Wang
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  3. Dhanvin Mehta
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  4. Edwin Olson
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Correspondence to Ryan J. Marcotte.

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This is one of the several papers published in Autonomous Robots comprising Special Issue on Robot Communication Challenges: Real-World Problems, Systems, and Methods.

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Marcotte, R.J., Wang, X., Mehta, D. et al. Optimizing multi-robot communication under bandwidth constraints. Auton Robot 44, 43–55 (2020). https://doi.org/10.1007/s10514-019-09849-0

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