Skip to main content
SpringerLink
Log in

Multi-group localization problem of service robots based on hybrid external localization algorithm with application to shopping mall environment

  • Review Article
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

Intellectualization of life is a general tendency due to the proliferation of technology and science. Based on this concept, this paper presents multi-group localization algorithms and detection algorithms for multi-group service robot system (MGSR). Shopping cart problem is considered as an exemplary multi-group service robot system. The MGSR is designed to provide users with co-service by multiple carts and allows multiple users operation simultaneously. In MGSR, a cart carrying personal belongings of the user follows the user automatically and provides real-time position information to the user. To fulfill estimating the location of MGSR, hybrid external localization algorithm based on combination of QR location information and ZigBee location estimate is proposed. To detect and track a cart by another cart with LRF, we define cart features in LRF data and employ a support vector data description method. Recognition of user–cart groups in MGSR is realized by ZigBee blind nodes on the cart. We verified the feasibility of the proposed algorithms for MGSR through three experiment trials.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

References

  1. Meng QH, Sun YC, Cao ZL (2000) Adaptive extended Kalman filter (AEKF)-based mobile robot localization using sonar. Robotica 18(05):459–473

    Article  Google Scholar 

  2. Hsu CH, Yu CH (2009) An accelerometer based approach for indoor localization. In: Proceedings of symposia and workshops on UIC-ATC Ubiquitous, autonomic and trusted computing, pp 223–227

  3. Hijikata S, Terabayashi K, Umeda K (2009) A simple indoor self-localization system using infrared LEDs. In: Proceedings of IEEE Conf. on networked sensing systems, pp 1–7

  4. Haverinen J, Kemppainen A (2009) Global indoor self-localization based on the ambient magnetic field. Robotics Auton Syst 57(10):1028–1035

    Article  Google Scholar 

  5. Kim D, Lee D et al (2014) Artificial landmark-based underwater localization for AUVs using weighted template matching. Intell Serv Robotics 7(3):175–184

    Article  Google Scholar 

  6. Biswas J, Veloso M (2012) Depth camera based indoor mobile robot localization and navigation. In: Proceedings of IEEE conf. on robotics and automation (ICRA), pp 1697–1702

  7. Gezici S, Tian Z et al (2005) Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks. Signal Process Mag IEEE 22(4):70–84

    Article  Google Scholar 

  8. Sanpechuda T, Kovavisaruch L (2008) A review of RFID localization: applications and techniques. In: Proceedings of IEEE conf. on electrical engineering/electronics, computer, telecommunications and information technology, pp 769–772

  9. Hu JS, Chan CY, Wang et al (2011) Simultaneous localization of a mobile robot and multiple sound sources using a microphone array. Adv Robotics 25(1–2):135–152

    Article  Google Scholar 

  10. Surmann H, Nüchter A, Hertzberg J (2003) An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments. Robotics Auton Syst 45(3):181–198

    Article  Google Scholar 

  11. Raghavan AN, Ananthapadmanaban H et al (2010) Accurate mobile robot localization in indoor environments using bluetooth. In: Proceedings of IEEE conf. on robotics and automation (ICRA), pp 4391–4396

  12. Paul AS, Wan EA (2008) Wi-Fi based indoor localization and tracking using sigma-point Kalman filtering methods. In: Proceedings of IEEE/ION conf. on position, location and navigation symposium, pp 646–659

  13. Gai S, Jung EJ, Yi BJ (2014) Localization algorithm based on Zigbee wireless sensor network with application to an active shopping cart. In: Proceedings of IEEE/RSJ Conf. on intelligent robots and systems, pp 4571–4576

  14. Bona B, Carlone L et al (2014) Supervision and monitoring of logistic spaces by a cooperative robot team: methodologies, problems, and solutions. Intell Serv Robotics 7(4):185–202

    Article  Google Scholar 

  15. Diosi A, Kleeman L (2004) Advanced sonar and laser range finder fusion for simultaneous localization and mapping. In: Proceedingsof IEEE/RSJ conf. on intelligent robots and systems, pp 1854–1859

  16. Kim HH, Ha KN et al (2009) Resident location-recognition algorithm using a Bayesian classifier in the PIR sensor-based indoor location-aware system. Syst Man Cybern Part C Appl Rev IEEE Trans 39(2):240–245

    Article  MathSciNet  Google Scholar 

  17. Luo RC, Chen O (2013) Wireless and pyroelectric sensory fusion system for indoor human/robot localization and monitoring. Mechatron IEEE/ASME Trans 18(3):845–853

  18. Ji Y, Yamashita A, Asama H (2015) RGB-D SLAM using vanishing point and door plate information in corridor environment. Intell Serv Robotics 8(2):105–114

    Article  Google Scholar 

  19. Schmitt T, Hanek R et al (2002) Cooperative probabilistic state estimation for vision-based autonomous mobile robots. Robotics Autom IEEE Trans 18(5):670–684

    Article  MATH  Google Scholar 

  20. Rone W, Ben-Tzvi P (2013) Mapping, localization and motion planning in mobile multi-robotic systems. Robotica 31(01):1–23

    Article  Google Scholar 

  21. Jeong D, Lee KIJU (2013) Directional RSS-based localization for multi-robot applications. In: Proceedings of WSEAS conf. on signal processing, robotics, and automation

  22. Franchi A, Oriolo G, Stegagno P (2010) Probabilistic mutual localization in multi-agent systems from anonymous position measures. In: Proceedings of IEEE conf. on decision and control (CDC), pp 6534–6540

  23. Roumeliotis S, Bekey GA (2002) Distributed multirobot localization. Robotics Autom IEEE Trans 18(5):781–795

    Article  Google Scholar 

  24. Dellaert F, Kipp A, Krauthausen P (2005) A multifrontal QR factorization approach to distributed inference applied to multirobot localization and mapping. In: Proceedings of the national conference on artificial intelligence, pp 1261–1266

  25. Dedeoglu G, Sukhatme GS (2000) Landmark-based matching algorithm for cooperative mapping by autonomous robots. Distrib Auton Robotic Syst 4:251–260

    Google Scholar 

  26. Burgard W, Moors M et al (2000) Collaborative multi-robot exploration. In: Proceedings of IEEE conf. on robotics and automation, pp 476–481

  27. Rekleitis IM, Dudek G, Milios EE (2000) Graph-based exploration using multiple robots. Distrib Auton Robotic Syst 4:241–250

    Google Scholar 

  28. Schultz AC, Parker LE (eds) (2003) Multi-robot systems: from swarms to intelligent automata, vol II. Springer, Netherlands

  29. Arai T, Pagello E, Parker LE (2002) Editorial: advances in multi-robot systems. IEEE Trans Robotics Autom 18(5):655–661

    Article  Google Scholar 

  30. Gai S, Oh SM, Yi BJ (2015) ASC localization in noisy environment based on wireless sensor network. Intell Serv Robotics 8(4):1–13

    Google Scholar 

  31. Fu G, Corradi P, Menciassi A, Dario P (2011) An integrated triangulation laser scanner for obstacle detection of miniature mobile robots in indoor environment. Mechatron IEEE/ASME Trans 16(4):778–783

  32. Arras KO, Mozos ÓM, Burgard W (2007) Using boosted features for the detection of people in 2d range data. In: Proceeding of IEEE Conf. on Robotics and Automation, pp 3402–3407

  33. Jung EJ, Lee JH, Yi BJ et al (2014) Development of a laser-range-finder-based human tracking and control algorithm for a marathoner service robot. Mechatron IEEE/ASME Trans 19(6):1963–1976

  34. SemwalVB Katiyar SA, Chakraborty R, Nandi GC (2015) Biologically-inspired push recovery capable bipedal locomotion modeling through hybrid automata. Robotics Auton Syst 70:181–190

    Article  Google Scholar 

  35. Tax DM, Duin RP (1999) Support vector domain description. Pattern Recognit Lett 20(11):1191–1199

    Article  Google Scholar 

  36. Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley

Download references

Acknowledgments

This work is supported by the Technology Innovation Program (10040097) funded by the Ministry of Trade, Industry and Energy Republic of Korea (MOTIE, Korea), supported by the Technology Innovation Program (10049789, Steering and driving mechanism for Cardio-vascular intervention procedure) funded By the Ministry of Trade, Industry & Energy (MI, Korea), supported by the Technology Innovation Program (10052980, Development of microrobotic system for surgical treatment of chronic total occlusion) funded By the Ministry of Trade, Industry & Energy (MI, Korea), and supported by Mid-career Researcher Program through NRF grant funded by the MEST (NRF-2013 R1A2A2A01068814). This work performed by ICT based Medical Robotic Systems Team of Hanyang University, Department of Electronic Systems Engineering was supported by the BK21 Plus Program funded by National Research Foundation of Korea (NRF).

Author information

Authors and Affiliations

  1. Department of Electronic Systems Engineering, Hanyang University, Seoul, Korea

    Shengnan Gai & Byung-Ju Yi

  2. Department of Electronic, Electrical, Control and Instrumentation Engineering, Hanyang University, Seoul, Korea

    Eui-Jung Jung

Authors
  1. Shengnan Gai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eui-Jung Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Byung-Ju Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Byung-Ju Yi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gai, S., Jung, EJ. & Yi, BJ. Multi-group localization problem of service robots based on hybrid external localization algorithm with application to shopping mall environment. Intel Serv Robotics 9, 257–275 (2016). https://doi.org/10.1007/s11370-016-0198-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-016-0198-1

Keywords

Search

Navigation