Autonomous Robots

, Volume 42, Issue 4, pp 739–759 | Cite as

Searching and tracking people with cooperative mobile robots

  • Alex GoldhoornEmail author
  • Anaís Garrell
  • René Alquézar
  • Alberto Sanfeliu
Part of the following topical collections:
  1. Special Issue: Online Decision Making in Multi-Robot Coordination


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.


Multi-robot coordination Urban robotics Search-and-track Decentralized coordination 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Institut de Robòtica i Informàtica Industrial (CSIC-UPC)BarcelonaSpain

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