Intelligent Service Robotics

, Volume 1, Issue 3, pp 221–235 | Cite as

Dynamic localization with hybrid trilateration for mobile robots in intelligent space

  • Kong-Woo LeeEmail author
  • Jae-Byung Park
  • Beom-Hee Lee
Original Research Paper


In this paper, we propose a new localization algorithm based on a hybrid trilateration algorithm for obtaining an accurate position of a robot in intelligent space. The proposed algorithm is also able to estimate a position of the moving robot by using the extended Kalman filter, taking into consideration time synchronization and velocity of the robot. For realizing the localization system, we employ several smart sensors as beacons on the ceiling in intelligent space and as a listener attached to the robot. Finally, simulation results show the feasibility and effectiveness of the proposed localization algorithm compared with existing trilateration algorithms.


Mobile robot Hybrid trilateration Dynamic localization Intelligent space 


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  1. 1.
    Siegwart R, Nourbakhsh IR (2004) Introduction to autonomous mobile robots, A Bradford Book. The MIT Press,Google Scholar
  2. 2.
    Everett HR (1995) Sensors for mobile robots, theory and applications. A. K. Peters, Ltd., New YorkGoogle Scholar
  3. 3.
    Chong H, Walker S, Eiamsa-Ard K, Burdick J (2000) Sensor-based exploration: incremental construction of the hierarchical generalized voronoi graph. Int J Robot Res 19: 126–148CrossRefGoogle Scholar
  4. 4.
    Russel S, Norvig P (1995) Artificial intelligence, a modern approach. Prentice Hall InternationalGoogle Scholar
  5. 5.
    Latombe J-C, Barraquand J (1991) Robot motion planning: a distributed presentation approach. Int J Robot Res 10: 628–649CrossRefGoogle Scholar
  6. 6.
    Latombe J-C (1991) Robot motion planning. Kluwer Academic Publishers, NorwoodGoogle Scholar
  7. 7.
    Lee JH, Lee BH, Choi MH (1998) A real time traffic control scheme of multiple AGV systems for collision free minimum time motion: a routing table approach. IEEE Trans Syst Man Cybernet Part A Syst Humans 28(3): 347–358CrossRefGoogle Scholar
  8. 8.
    Park JB, Lee JH, Lee BH (2006) Online turnover-free control for a mobile agent with a terrain prediction sensor. J Field Robot 23(1): 59–77CrossRefGoogle Scholar
  9. 9.
    Park SH, Lee BH (2006) A new analytical representation to robot path generation with collision avoidance through the use of the collision map. Int J Control Automat Syst 4(1): 77–86Google Scholar
  10. 10.
    Park JB, Lee JH, Lee BH (2006) Rollover-free navigation for a mobile agent in an unstructured environment. IEEE Trans Syst Man Cybernet Part B: Cybernet 36(3): 835–848CrossRefGoogle Scholar
  11. 11.
    Park SH, Lee BH (2006) Analysis of robot collision characteristics using the concept of the collision map. Robotica 24: 295–303CrossRefMathSciNetGoogle Scholar
  12. 12.
    Priyantha NB (2005) The cricket indoor location system. Ph.D. Thesis, Massachusetts Institute of Technology, June 2005Google Scholar
  13. 13.
    Getting I (1993) The global positioning system. IEEE Spectr 30(12): 36–47CrossRefGoogle Scholar
  14. 14.
    Hecht E (2002) Optics 4th edn. Addison-Wesley, ReadingGoogle Scholar
  15. 15.
    Merrill IS (2002) Introduction to radar systems 3rd edn. McGraw-Hill, New YorkGoogle Scholar
  16. 16.
    Hoffmann-Wellenhof B, Lichtenegger H, Collins J (1997) Global positioning system: theory and practice 4th edn. Springer, New YorkGoogle Scholar
  17. 17.
    Enhanced 911 services.
  18. 18.
    Helfrick A (2004) Principles of avionics 3rd edn. Avionics Communications Inc., LeesburgGoogle Scholar
  19. 19.
    Pierce JA (1990) An introduction to Loran. Aerospace Electr Syst Mag IEEE 5(10): 16–33CrossRefGoogle Scholar
  20. 20.
    Doherty L, Pister K, Ghaoui L (2001) Convex position estimation in wireless sensor networks. In: Proceedings IEEE INFOCOM, pp. 1655–1663, Anchorage, AK, April 2001Google Scholar
  21. 21.
    Ladd AM, Bekris KE, Rudys A, Marceau G, Kavraki LE, Wallach DS (2002) Robotics-based location sensing using wireless Ethernet. In: Proceedings of the 8th ACM MOBICOM Conference, pp. 227–238, Atlanta, GA, September 2002Google Scholar
  22. 22.
    Harter A, Hopper A (1997) A new location technique for the active office. IEEE Personal Communi 4(5): 43–47Google Scholar
  23. 23.
    Want R, Hopper A, Falcao V, Gibbons J (1992) The active badge location system. ACM Trans Informat Syst 10(1): 91–102CrossRefGoogle Scholar
  24. 24.
    Welch G, Bishop G (1997) SCAAT: Incremental tracking with incomplete information. Comput Graphics 31: 333–344Google Scholar
  25. 25.
    Chung WC, Ha DS (2003) An accurate Ultra Wideband (UWB) ranging for precision asset location. In: International conference on ultra wideband systems and technologies, Reston, VA, Novomber 2003 pp. 389–393Google Scholar
  26. 26.
    Hazas M, Ward A (2002) A novel broadband ultrasonic location system. In: Proceedings of UbiComp 2002: ubiquitous computing, Goteborg, Sweden, Septemper 2002 pp. 264–280Google Scholar
  27. 27.
    Girod L, Estrin D (2001) Robust range estimation using acoustic and multimodal sensing. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), Maui, HI, October 2001Google Scholar
  28. 28.
    Navarro-Serment LE, Paredis CJJ, Khosla P (1999) A beacon system for the localization of distributed robotic teams. In: Proceedings of the international conference field and service robots, Pittsburgh, PA, August 1999, pp. 232–237Google Scholar
  29. 29.
    Bruyninckx H (1999) Forward kinematics for Hunt–Primrose parallel manipulators. Mech Mach Theory 34: 657–664zbMATHCrossRefMathSciNetGoogle Scholar
  30. 30.
    Manolakis DE (1996) Efficient solution and performance analysis of 3-D position estimation by trilateration. IEEE Trans Aerosp Electron Syst 32: 1239–1248CrossRefGoogle Scholar
  31. 31.
    Mackay AL (1974) Generalized structural geometry. Acta Crystalograph A 30: 440–447CrossRefGoogle Scholar
  32. 32.
    Eberly D (1996) Finding the intersection of three spheres, newsgroupGoogle Scholar
  33. 33.
    Thomas F, Ros L (2005) Revisiting trilateration for robot localization. IEEE Trans Robot 1(1): 93–101CrossRefMathSciNetGoogle Scholar
  34. 34.
    Cayley A (1841) A theorem in the geometry of position. Cambridge Math J 2: 267–271Google Scholar
  35. 35.
    Cayley A (1963) A theorem in the geometry of position. In: Collected mathematical papers of arthur cayley. Cambridge University Press, CambridgeGoogle Scholar
  36. 36.
    Coope ID (2000) Reliable computation of the points of intersection of n spheres in Rn. Austral, New Zealand Ind Appl Math J., Pt C, 42: 461–477MathSciNetGoogle Scholar
  37. 37.
    Balakrishnan H, Baliga R, Curtis D, Goraczko M, Miu A, Priyantha NB, Smith A, Steele K, Teller S, Wang K (2003) Lessons from developing and deploying the cricket indoor location system. November 2003Google Scholar
  38. 38.
    Blumenthal LM (1953) Theory and applications of distance geometry. Oxford University Press, OxfordzbMATHGoogle Scholar
  39. 39.
    Kleeman L (1992) Optimal estimation of position and heading for mobile robots using ultrasonic beacons and dead-reckoning. IEEE international conference on robotics and automation, Nice, France, pp mn2582–2587Google Scholar
  40. 40.
    Fletcher R (1987) Practical Methods of Optimization 2nd edn. John Wiley & Sons, New YorkzbMATHGoogle Scholar
  41. 41.
    Lee J-H, Ando N, Hashimoto H (1999) Design policy of intelligent space. Syst Man CybernetGoogle Scholar
  42. 42.
  43. 43.
    Chae HS, Lee JY, Yu WP, Doh NL (2005) StarLITE: a new artificial landmark for the navigation of mobile robots. In: Japan–Korea joint symposium on network robot systems November 2005Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.School of Electrical EngineeringSeoul National UniversitySeoulSouth Korea
  2. 2.Division of Electronics and Information EngineeringChonbuk National UniversityJeonjuSouth Korea

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