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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
Article
Part of the following topical collections:
  1. Special Issue: Online Decision Making in Multi-Robot Coordination

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.

Keywords

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

References

  1. Ahmad, A., & Lima, P. (2013). Multi-robot cooperative spherical-object tracking in 3D space based on particle filters. Robotics and Autonomous Systems, 61(10), 1084–1093. In selected papers from the 5th european conference on mobile robots (ECMR 2011).Google Scholar
  2. Amor-Martinez, A., Ruiz, A., Moreno-Noguer, F., & Sanfeliu, A. (2014). On-board Real-time Pose Estimation for UAVs using deformable visual contour registration. In Proceedings of the IEEE international conference in robotics and automation (ICRA).Google Scholar
  3. Arras, K. O., Mozos, O. M., & Burgard, W. (2007). Using boosted features for the detection of people in 2D range data. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 3402–3407).Google Scholar
  4. Arulampalam, M., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.CrossRefGoogle Scholar
  5. Blackman, S. S. (2004). Multiple hypothesis tracking for multiple target tracking. IEEE Aerospace and Electronic Systems Magazine, 19(1), 5–18.CrossRefGoogle Scholar
  6. Brscic, D., Kanda, T., Ikeda, T., & Miyashita, T. (2013). Person tracking in large public spaces using 3-d range sensors. IEEE Transactions on Human-Machine Systems, 43(6), 522–534.CrossRefGoogle Scholar
  7. Burgard, W., Moors, M., Stachniss, C., & Schneider, F. E. (2005). Coordinated multi-robot exploration. IEEE Transactions on Robotics, 21(3), 376–386.CrossRefGoogle Scholar
  8. Capitan, J., Merino, L., & Ollero, A. (2016). Cooperative decision-making under uncertainties for multi-target surveillance with multiples UAVs. Journal of Intelligent & Robotic Systems, 84(1), 371–386.CrossRefGoogle Scholar
  9. Charrow, B., Michael, N., & Kumar, V. (2013). Cooperative multi-robot estimation and control for radio source localization. In P. J. Desai, G. Dudek, O. Khatib, & V. Kumar (Eds.), Experimental robotics: The 13th international symposium on experimental robotics (pp. 337–351). Heidelberg: Springer.CrossRefGoogle Scholar
  10. Choi, W., Pantofaru, C., & Savarese, S. (2011). Detecting and tracking people using an RGB-D camera via multiple detector fusion. In Workshop on challenges and opportunities in robot perception (in conjunction with ICCV-11).Google Scholar
  11. Chung, T., Hollinger, G., & Isler, V. (2011). Search and pursuit-evasion in mobile robotics. Autonomous Robots, 31(4), 299–316.CrossRefGoogle Scholar
  12. Cui, J., Zha, H., Zhao, H., & Shibasaki, R. (2008). Multi-modal tracking of people using laser scanners and video camera. Image and Vision Computing, 26(2), 240–252.CrossRefGoogle Scholar
  13. Ferrein, A., & Steinbauer, G. (2016). 20 years of robocup. KI - Künstliche Intelligenz, 30(3), 225–232.CrossRefGoogle Scholar
  14. Garrell, A., & Sanfeliu, A. (2012). Cooperative social robots to accompany groups of people. The International Journal of Robotics Research, 31(13), 1675–1701.CrossRefGoogle Scholar
  15. Garrell, A., Villamizar, M., Moreno-Noguer, F., & Sanfeliu, A. (2013). Proactive behavior of an autonomous mobile robot for human-assisted learning. In Proceedings of IEEE RO-MAN (pp. 107–113).Google Scholar
  16. Glas, D. F., Morales, Y., Kanda, T., Ishiguro, H., & Hagita, N. (2015). Simultaneous people tracking and robot localization in dynamic social spaces. Autonomous Robots, 39(1), 43–63.CrossRefGoogle Scholar
  17. Goldhoorn, A., Alquézar, R., & Sanfeliu, A. (2013a). Analysis of methods for playing human robot hide-and-seek in a simple real world urban environment. In ROBOT (2), advances in intelligent systems and computing (Vol. 253, pp. 505–520). Springer.Google Scholar
  18. Goldhoorn, A., Alquézar, R., & Sanfeliu, A. (2013b). Comparison of MOMDP and heuristic methods to play hide-and-seek. In K. Gibert, V. J. Botti, & R. R. Bolaño (Eds.), CCIA Frontiers in artificial intelligence and applications (Vol. 256, pp. 31–40). Amsterdam: IOS Press.Google Scholar
  19. Goldhoorn, A., Garrell, A., Alquézar, R., & Sanfeliu, A. (2014). Continuous real time pomcp to find-and-follow people by a humanoid service robot. In Proceedings of the IEEE-RAS international conference on humanoid robots (pp. 741–747).Google Scholar
  20. Grisetti, G., Stachniss, C., & Burgard, W. (2007). Improved techniques for grid mapping with rao-blackwellized particle filters. Journal IEEE Transactions on Robotics, 23(1), 34–46.CrossRefGoogle Scholar
  21. Hlinka, O., Hlawatsch, F., & Djuric, P. M. (2013). Distributed particle filtering in agent networks: A survey, classification, and comparison. IEEE Signal Processing Magazine, 30, 61–81.CrossRefGoogle Scholar
  22. Hollinger, G., Yerramalli, S., Singh, S., Mitra, U., & Sukhatme, G. (2015). Distributed data fusion for multirobot search. IEEE Transactions on Robotics, 31(1), 55–66.CrossRefGoogle Scholar
  23. Hollinger, G. A., Singh, S., & Kehagias, A. (2010). Improving the efficiency of clearing with multi-agent teams. International Journal of Robotics Research, 29(8), 1088–1105.CrossRefGoogle Scholar
  24. Johansson, E., & Balkenius, C. (2005). It’s a child’s game: Investigating cognitive development with playing robots. In Proceedings of the 4th international conference on development and learning (pp. 164–164).Google Scholar
  25. Kurniawati, H., Hsu, D., & Lee, W. (2008). SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces. In Proceedings of Robotics: Science and Systems IV, Zurich, Switzerland.Google Scholar
  26. Lian, F. L., Chen, C. L., & Chou, C. C. (2015). Tracking and following algorithms for mobile robots for service activities in dynamic environments. International Journal of Automation and Smart Technology, 5(1), 49–60.Google Scholar
  27. Linder, T., Breuers, S., Leibe, B., & Arras, K. O. (2016). On multi-modal people tracking from mobile platforms in very crowded and dynamic environments. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 5512–5519).Google Scholar
  28. Luber, M., Sinello, L., & Arras, K. (2011). People tracking in RGB-D data with on-line boosted target models. In Proceedings of the IEEE international conference on intelligent robots and systems (IROS) (pp. 3844–3849).Google Scholar
  29. Marconi, L., Melchiorri, C., Beetz, M., Pangercic, D., Siegwart, R., Leutenegger, S., Carloni, R., Stramigioli, S., Bruyninckx, H., Doherty, P., Kleiner, A., Lippiello, V., Finzi, A., Siciliano, B., Sala, A., & Tomatis, N. (2012). The SHERPA project: Smart collaboration between humans and ground-aerial robots for improving rescuing activities in alpine environments. In Proceedings of the IEEE international symposium on safety, security, and rescue robotics (SSRR) (pp. 1–4).Google Scholar
  30. Montemerlo, M., Thrun, S., & Whittaker, W. (2002). Conditional particle filters for simultaneous mobile robot localization and people-tracking. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (Vol. 1, pp. 695–701). IEEE.Google Scholar
  31. Ong, S. C. W., Png, S. W., Hsu, D., & Lee, W. S. (2010). Planning under uncertainty for robotic tasks with mixed observability. International Journal of Robotics Research, 29(8), 1053–1068.CrossRefGoogle Scholar
  32. Oyama, T., Yoshida, E., Kobayashi, Y., & Kuno, Y. (2013). Tracking visitors with sensor poles for robot’s museum guide tour. In Proceedings of the 6th international conference on human system interactions (HSI) (pp. 645–650). IEEE.Google Scholar
  33. Pineau, J., Gordon, G., & Thrun, S. (2003). Point-based value iteration: An anytime algorithm for POMDPs. In Proceedings of the international joint conference on artificial intelligence (pp. 477–484).Google Scholar
  34. Sanfeliu, A., Andrade-Cetto, J., Barbosa, M., Bowden, R., Capitán, J., Corominas, A., et al. (2010). Decentralized sensor fusion for ubiquitous networking robotics in urban areas. Sensors, 10(3), 2274–2314.CrossRefGoogle Scholar
  35. Sheh, R., Schwertfeger, S., & Visser, A. (2016). 16 years of robocup rescue. KI - Künstliche Intelligenz, 30(3), 267–277.CrossRefGoogle Scholar
  36. Sheng, X., Hu, Y. H., & Ramanathan, P. (2005). Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network. In Proceedings of the 4th international symposium on information processing in sensor networks (IPSN), IEEE Press, Piscataway, NJ, USA.Google Scholar
  37. Silver, D., & Veness, J. (2010). Monte-Carlo planning in large POMDPs. In Proceedings of 24th advances in neural information processing systems (NIPS) (pp. 1–9).Google Scholar
  38. Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001). Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 128(1–2), 99–141.CrossRefzbMATHGoogle Scholar
  39. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics (intelligent robotics and autonomous agents). Cambridge: The MIT Press.zbMATHGoogle Scholar
  40. Volkhardt, M., & Gross, H. M. (2013). Finding people in apartments with a mobile robot. In IEEE international conference on systems, man, and cybernetics (pp. 4348–4353).Google Scholar
  41. Vázquez, M. A., & Míguez, J. (2017). A robust scheme for distributed particle filtering in wireless sensors networks. Signal Processing, 131, 190–201.CrossRefGoogle Scholar
  42. Xu, Z., Fitch, R., & Sukkarieh, S. (2013). Decentralised coordination of mobile robots for target tracking with learnt utility models. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 2014–2020). IEEE.Google Scholar

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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