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Scalable multirobot planning for informed spatial sampling

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Abstract

This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatials fields. We address the problem of efficient data collection using multiple autonomous vehicles and consider the effects of communication between multiple robots, acting independently, on the overall sampling performance of the team. We focus on the distributed sampling problem where the robots operate independent of their teammates, but have the ability to communicate their current state to other neighbors within a fixed communication range. Our proposed approach is scalable and adaptive to various environmental scenarios, changing robot team configurations, and runs in real-time, which are important features for many real-world applications. We compare the performance of our proposed algorithm to baseline strategies through simulated experiments that utilize models derived from both synthetic and field deployment data. The results show that our sampling algorithm is efficient even when robots in the team are operating with a limited communication range, thus demonstrating the scalability of our method in sampling large-scale environments.

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Notes

  1. The robots are assumed to be homogeneous in terms of their capabilities. They may all have the same set of learnt parameters. However, this is not compulsory as long as they are all trained on similar distributions.

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Authors and Affiliations

  1. GRASP Laboratory, University of Pennsylvania, Philadelphia, PA, USA

    Sandeep Manjanna & M. Ani Hsieh

  2. Center for Intelligent Machines, McGill University, Montreal, QC, Canada

    Greogory Dudek

Authors
  1. Sandeep Manjanna
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  2. M. Ani Hsieh
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  3. Greogory Dudek
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Correspondence to Sandeep Manjanna.

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This is one of the several papers published in Autonomous Robots comprising the Special Issue on Robot Swarms in the Real World: from Design to Deployment.

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Manjanna, S., Hsieh, M.A. & Dudek, G. Scalable multirobot planning for informed spatial sampling. Auton Robot 46, 817–829 (2022). https://doi.org/10.1007/s10514-022-10048-7

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