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Searching and tracking people with cooperative mobile robots

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Abstract

Social robots should be able to search and track people in order to help them. In this paper we present two different techniques for coordinated multi-robot teams for searching and tracking people. A probability map (belief) of a target person location is maintained, and to initialize and update it, two methods were implemented and tested: one based on a reinforcement learning algorithm and the other based on a particle filter. The person is tracked if visible, otherwise an exploration is done by making a balance, for each candidate location, between the belief, the distance, and whether close locations are explored by other robots of the team. The validation of the approach was accomplished throughout an extensive set of simulations using up to five agents and a large amount of dynamic obstacles; furthermore, over three hours of real-life experiments with two robots searching and tracking were recorded and analysed.

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Authors and Affiliations

  1. Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, 08028, Barcelona, Spain

    Alex Goldhoorn, Anaís Garrell, René Alquézar & Alberto Sanfeliu

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  1. Alex Goldhoorn
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  2. Anaís Garrell
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  3. René Alquézar
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  4. Alberto Sanfeliu
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Corresponding author

Correspondence to Alex Goldhoorn.

Additional information

This is one of the several papers published in Autonomous Robots comprising the Special Issue on Online Decision Making in Multi-Robot Coordination.

Work partially supported by the Spanish Ministry of Science and Innovation under project Rob-In-Coop (DPI2013-42458-P) and EU FP7 project ARCAS (INFSO-ICT-287617).

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Goldhoorn, A., Garrell, A., Alquézar, R. et al. Searching and tracking people with cooperative mobile robots . Auton Robot 42, 739–759 (2018). https://doi.org/10.1007/s10514-017-9681-6

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