Taxonomy of Navigation for First Responders

  • Zhiyong WangEmail author
  • Sisi Zlatanova
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Navigation services are gaining much importance for all kind of human activities ranging from tourist navigation to support of rescue teams in disaster management. With the frequent natural disasters occurring in recent years, emergency navigation for first responders poses a set of serious challenges for researchers in the navigation field. The chapter introduces a taxonomy of navigation among obstacles, categorizes navigation cases on basis of type and multiplicity of first responders, destinations, and obstacles, and reviews related research. This review reveals limitations in current navigation research and challenges that have not been explored yet. We also briefly present our approach using agent-based technology, real measurements and web technologies for the development and implementation of navigation systems that aim at navigating first responders among both static and moving obstacles. Finally, we conclude by providing views on further investigations and developments.


Taxonomy Navigation First responders 


  1. Belkhouche F, Belkhouche B, Rastgoufard P (2007) Parallel navigation for reaching a moving goal by a mobile robot. Robotica 25(1):63–74CrossRefGoogle Scholar
  2. Bowling M, Veloso M (1999) Motion control in dynamic multi-robot environments. In: IEEE international symposium on computational intelligence in robotics and automation. CIRA’99, Monterey, USA, pp 168–173Google Scholar
  3. De Almeida VT, Güting RH (2005) Indexing the trajectories of moving objects in networks. GeoInformatica 9(1):33–60CrossRefGoogle Scholar
  4. Faigl J (2011) On the performance of self-organizing maps for the non-euclidean traveling salesman problem in the polygonal domain. Inf Sci Int J 181(19):4214–4229Google Scholar
  5. Girard G, Côté S, Zlatanova S, Barette Y, St-Pierre J, Van Oosterom P (2011) Indoor pedestrian navigation using foot-mounted IMU and portable ultrasound range sensors. Sensors 11(8):7606–7624CrossRefGoogle Scholar
  6. Guntsch M, Middendorf M, Schmeck H (2001) An Ant colony optimization approach to dynamic TSP. In: Proceedings of the genetic and evolutionary computation conference (GECCO-2001), San Francisco, USA, pp 860–867Google Scholar
  7. Johnson R (2008) GIS technology and applications for the fire services. In: Zlatanova S, Li J (eds) Geospatial information technology for emergency response. Taylor & Francis Ltd, London, pp 351–372Google Scholar
  8. Kapoor S, Maheshwari SN, Mitchell JSB (1997) An efficient algorithm for euclidean shortest paths among polygonal obstacles in the plane. Discrete Comput Geom 18(4):377–383CrossRefGoogle Scholar
  9. Khoury HM, Kamat VR (2009) Evaluation of position tracking technologies for user localization in indoor construction environments. Autom Constr 18(4):444–457CrossRefGoogle Scholar
  10. Kulich M, Kubalik J, Kléma J, Faigl J (2004) Rescue operation planning by soft computing techniques. In: IEEE 4th international conference on intelligent systems design and application, Budapest, Hungary, pp 103–109Google Scholar
  11. Kunwar F, Wong F, Mrad RB, Benhabib B (2006) Guidance-based on-line robot motion planning for the interception of mobile targets in dynamic environments. J Intell Rob Syst 47(4):341–360CrossRefGoogle Scholar
  12. Li N, Becerik-Gerber B (2011) Performance-based evaluation of rfid-based indoor location sensing solutions for the built environment. Adv Eng Inform 25(3):535–546CrossRefGoogle Scholar
  13. Li F, Klette R (2006) Finding the shortest path between two points in a simple polygon by applying a rubberband algorithm. Adv Image Video Technol 4319:280–291CrossRefGoogle Scholar
  14. Li H, Yang SX, Seto ML (2009) Neural-network-based path planning for a multirobot system with moving obstacles. IEEE Trans Syst Man Cybern Part C Appl Rev 39(4):410–419CrossRefGoogle Scholar
  15. Masehian E, Katebi Y (2007) Robot motion planning in dynamic environments with moving obstacles and target. Int J Mech Syst Sci Eng 1(1):20–25Google Scholar
  16. Meratnia N (2005) Towards database support for moving object data, PhD thesis, University of TwenteGoogle Scholar
  17. Mitchell JSB (1993) Shortest paths among obstacles in the plane. In: Proceedings of the 9th annual symposium on computational geometry, ACM, pp 308–317Google Scholar
  18. Mitchell JSB, Rote G, Woeginger G (1992) Minimum-link paths among obstacles in the plane. Algorithmica 8(1):431–459CrossRefGoogle Scholar
  19. Nedkov S, Zlatanova S (2011) Enabling obstacle avoidance for Google maps’ navigation service. Gi4DM 2011: geoinformation for disaster management, Anltalya, TurkeyGoogle Scholar
  20. Ni J, Yang SX (2011) Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments. IEEE Trans Neural Networks 22(12):2062–2077CrossRefGoogle Scholar
  21. Parker CJ, Macfarlane R, Phillips C (2008) Integrated emergency management: experiences and challenges of a National geospatial information provider, Ordnance Survey. In: Zlatanova S, Li J (eds) Geospatial information technology for emergency response, vol 6. Taylor & Francis, London, pp 275–310Google Scholar
  22. Schmitz S, Zipf A, Neis P (2008) New applications based on collaborative geodata-the case of routing. In: XXVIII INCA international congress on collaborative mapping and space technology, Gandhinagar, IndiaGoogle Scholar
  23. Sistla AP, Wolfson O, Chamberlain S, Dao S (1997) Modeling and querying moving objects. In: Proceedings of the 13th international conference on data engineering, Birmingham, pp 422–432Google Scholar
  24. Togt R, Beinat E, Zlatanova S, Scholten HJ (2005) Location interoperability services for medical emergency operations during disasters. Geo-information for disaster management. Springer, Heidelberg, pp 1127–1141Google Scholar
  25. Undeger C, Polat F (2010) Multi-agent real-time pursuit. Auton Agent Multi-Agent Syst 21(1):69–107CrossRefGoogle Scholar
  26. Van Bemmelen J, Quak W, Van Hekken M, Van Oosterom P (1993) Vector versus raster-based algorithms for cross country movement planning. Auto-Carto 11, Minneapolis, USA, pp 304–317Google Scholar
  27. Visser I (2009) Route determination in disaster areas, Msc thesis, Utrecht UniversityGoogle Scholar
  28. Wolfson O, Xu B, Chamberlain S, Jiang L (1998) Moving objects databases: issues and solutions. Proceedings of 10th international conference on scientific and statistical database management, Capri, Italy, pp 111–122Google Scholar
  29. Yang SX, Hu Y, Meng MQH (2006) A knowledge based GA for path planning of multiple mobile robots in dynamic environments. In: 2006 IEEE conference on robotics, automation and mechatronics, Bangkok, pp 1–6Google Scholar
  30. Zlatanova S, Baharin SSK (2008) Optimal navigation of first responders using DBMS. In: Joint conference of the 3rd international conference on information systems for crisis response and management/4th international symposium on geo-information for disaster management, pp 541–554Google Scholar
  31. Zu D, Han JD, Campbell M (2004) Artificial potential guided evolutionary path plan for multi-vehicle multi-target pursuit. In: 2004 IEEE international conference on robotics and biomimetics, China, pp 855–861Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.OTB Research Institute for the Built EnvironmentTU delftDelftThe Netherlands

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