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Multi-robot information driven path planning under communication constraints

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Abstract

We consider the problem of simultaneous exploration and information collection in an initially unknown environment by multiple autonomous robots when the communication between robots is unreliable and intermittent. We propose a novel algorithm where decisions to select locations for exploration and information collection are guided by a utility function that combines Gaussian Process-based distributions for information entropy and communication signal strength, along with a distributed coordination protocol to avoid path conflicts between robots and repeated exploration and information collection from the same region by different robots. Our proposed algorithm was experimentally validated in simulation and on hardware Clearpath Jackal robots while using a realistic signal loss model from the literature. Our results show that our approach plans it samples such that it receives up to 25 dBm more signal strength throughout navigation compared to approaches that do not consider communications when selecting locations to sample from while accomplishing similar levels of model error.

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Notes

  1. Evolutionary-algorithm based techniques can be used to fine-tune these parameters to improve the algorithm’s performance.

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Authors and Affiliations

  1. Computer Science Department, University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE, 68182, USA

    Bradley Woosley & Prithviraj Dasgupta

  2. United States Army Research Laboratory, Adelphi, MD, USA

    John G. Rogers III & Jeffrey Twigg

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  1. Bradley Woosley
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  2. Prithviraj Dasgupta
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  3. John G. Rogers III
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  4. Jeffrey Twigg
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Correspondence to Bradley Woosley.

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Woosley, B., Dasgupta, P., Rogers, J.G. et al. Multi-robot information driven path planning under communication constraints. Auton Robot 44, 721–737 (2020). https://doi.org/10.1007/s10514-019-09890-z

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