Intelligent Service Robotics

, Volume 10, Issue 3, pp 229–240 | Cite as

Online complete coverage path planning using two-way proximity search

  • Amna Khan
  • Iram Noreen
  • Hyejeong Ryu
  • Nakju Lett Doh
  • Zulfiqar HabibEmail author
Original Research Paper


This paper presents an efficient online approach for complete coverage path planning of mobile robots in an unknown workspace based on online boustrophedon motion and an optimized backtracking mechanism. The presented approach first performs a single continuous boustrophedon motion until a critical point is reached. In order to completely cover the environment, next starting point is decided by using the accumulated knowledge of the environment map. An efficient backtracking technique based on proposed Two-way Proximity Search algorithm is used to plan a path from the critical point to the new starting point. Simulation results show the efficiency of proposed backtracking approach with improved total coverage time, coverage path length and memory requirements.


Complete coverage path Mobile robots Boustrophedon motions Two-way proximity search 



This study is supported by the research grant of Higher Education Commission (HEC) of Pakistan (No. 20-2359/NRPU/R&D/HEC/12-6779) and partly supported by a project of Korean government (No. 10073166).


  1. 1.
    Dakulovic M, Petrovic I (2012) Complete coverage path planning of mobile robots for humanitarian demining. Ind Robot: Int J 39(5):484–493CrossRefGoogle Scholar
  2. 2.
    Hameed AI, Bochtis D, Sorensen CA (2013) An optimized field coverage path planning approach for navigation of agricultural robots in fields involving obstacles. Int J Adv Robot Syst 10:1–9CrossRefGoogle Scholar
  3. 3.
    Waanders M (2011) Coverage path planning for mobile cleaning robots. In: 15th twente student conference on IT, EnschedeGoogle Scholar
  4. 4.
    Perezimaz HIA, Rezeck PAF, Macharet DG, Campos MFM (2016) Multi-robot 3D coverage path planning for First Responders teams. In: IEEE international conference on automation science and engineering (CASE), pp 1374–1379Google Scholar
  5. 5.
    Galceran E, Carreras M (2012) Efficient Seabed Coverage Path Planning for ASVs and AUVs. In: IEEE/RSJ international conference on intelligent robots and systems, Vilamoura, PortugalGoogle Scholar
  6. 6.
    Franco CD, Buttazzo G (2016) Energy-aware coverage path planning of UAVs. In: IEEE international conference on autonomous robot systems and competitions, pp 111–117Google Scholar
  7. 7.
    Choset H (2001) Coverage for robotics—a survey for recent results. Ann Math Artif Intell 31:13–126CrossRefGoogle Scholar
  8. 8.
    Choset H, Pignon P (1998) Coverage path planning: the boustrophedon cellular decomposition. In: Zelinsky A (ed) Field and service robotics. Springer, London, pp 203–209Google Scholar
  9. 9.
    Gabriely Y, Rimon E (2002) Spiral-STC : an on-line coverage algorithm of grid environments by a mobile robot. In: International conference on robotics and automation, pp 954–960Google Scholar
  10. 10.
    Qiu X, Liu S , Yang SX (2004) A rolling method for complete coverage path planning in uncertain environments. In: International conference on robotics and biometric, pp 146–151Google Scholar
  11. 11.
    Hameed IA, Bochtis DD, Sorensen CG (2011) Driving angle and track sequence optimization for operational path planning using genetic algorithms. Appl Eng Agric ASABE 27(6):1077–1086CrossRefGoogle Scholar
  12. 12.
    Chibin Z, Xingsong W, Yong D (2008) Complete coverage path Planning based on ant colony algorithm. In: 15th international conference on mechatronics and machine vision in practice, pp 357–361Google Scholar
  13. 13.
    Galceran E, Carreras M (2013) A survey on coverage path planning for robotics. Robots Auton Syst 61:1258–1276CrossRefGoogle Scholar
  14. 14.
    Janchiv A, Batsaikhan D, Kim B, Lee W, Lee S (2013) Time-efficient and complete coverage path planning based on flow networks for multi-robots. Int J Control Autom Syst 11(2):369–376CrossRefGoogle Scholar
  15. 15.
    Huang W H (2001) Optimal line-sweep-based decompositions for coverage algorithm. In: Proceedings of the 2001 IEEE international conference on robotics and automation, pp 27–32Google Scholar
  16. 16.
    Zhou P, Wang Z , Li Z , Li Y (2012) Complete coverage path planning of mobile robot based on dynamic programming algorithm. In: 2nd international conference on electronic and mechanical engineering and information technology, pp 1837–1841Google Scholar
  17. 17.
    Choset H, Lynch K, Hutchinson S, Kantor G, Burgard W, Kavraki I, Thrun S (2005) Principals of robot motion: theory, algorithms and implementations. MIT Press, CambridgezbMATHGoogle Scholar
  18. 18.
    Kim DH, Hoang G, Bae MJ, Kim JW, Yoon SM, Yeo TK, Sup H, Kim SB (2014) Path tracking control coverage of a mining robot based on exhaustive path planning with exact cell decomposition. In: International conference on control, automation and systems, pp 730–735Google Scholar
  19. 19.
    Liu Y, Lin X, Zho S (2008) Combined coverage path planning for autonomous cleaning robots in unstructured environments. In: Proceedings of the 7th world congress on intelligent control and automation, pp 8271–8276Google Scholar
  20. 20.
    Qiu X, Liu S , Yang S (2004) A rolling method for complete coverage path planning in uncertain environments. In: International conference on robotics and biometrics, pp 146–151Google Scholar
  21. 21.
    Weiss-Cohen M, Sirotin I, Rave E (2008) Lawn mowing system for known areas. In: International conference on computational intelligence for modeling control and automation, pp 539–544Google Scholar
  22. 22.
    Senthilkumar KS, Bharadwaj KK (2008) Spanning tree based terrain coverage by multi robots in an unknown environment. In: INDICON, pp 120–125Google Scholar
  23. 23.
    Senthilkumar KS, Bharadwaj KK (2012) Multi-robot exploration and terrain coverage in an unknown environment. Robot Auton Syst 60:123–132CrossRefGoogle Scholar
  24. 24.
    Zuo G, Zhang P , Qiao J (2010) Path planning algorithm based on sub-region for agricultural robot. In: International Asia conference on informatics in control, automation and robotics, pp 197–200Google Scholar
  25. 25.
    Jin J, Tang L (2010) Optimal coverage path planning for arable farming on 2D surfaces. Trans ASABE 53(1):283–295CrossRefGoogle Scholar
  26. 26.
    Viet HH, Dang VH, Laskar MNU, Chung TC (2013) BA* : an online complete coverage algorithm for cleaning robots. Int J Appl Intell Neural Netw Complex Probl Solving Technol 39:217235Google Scholar
  27. 27.
    Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107CrossRefGoogle Scholar
  28. 28.
    Stentz A (1994) Optimal and efficient path planning for partially-known environments. In: Proceedings IEEE conference on robotics and automationGoogle Scholar
  29. 29.
    Dakulovic M, Horvatic S, Petrovic I (2011) Complete coverage D* algorithm for path planning of a floor-cleaning mobile robot. In: 18th international federation of automatic control (IFAC) world congress, pp 5950–5955Google Scholar
  30. 30.
    Witkowsky C (1983) A parallel processor algorithm for robot route planning. In: International joint conference on artificial intelligence, pp 827–829Google Scholar
  31. 31.
    Zelinsky A , Jarvis RA , Byrne JC , Yuta S (1993) Planning paths of complete coverage of an unstructured environment by a mobile-robot. In: Proceedings of international conference on advanced roboticsGoogle Scholar
  32. 32.
    Dijkstra E (1958) A note on two problems in connexion with graphs. Numerische Mathematik 1(1):269–271MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Botean A, Müller M, Schaeffer J (2004) Near optimal hierarchical path-finding. J Game Dev 1:7–28Google Scholar
  34. 34.
    Nash A, Daniel K, Koenig S, Felner A (2010) Theta*: any angle path planning on grids. J Artif Intell Res 39(1):533–579MathSciNetzbMATHGoogle Scholar
  35. 35.
    Yang K ,Sukkareih S (2008) 3D Smooth path planning for a UAV in cluttered natural environment. In International conference on intelligent robots and systems, pp 794–800Google Scholar
  36. 36.
    Pratama PS, Kim JW, Kim HK, Yoon SM, Yeu TK, Hong S, Oh SJ, Kim SB (2015) Path planning algorithm to minimize an overlapped path and turning number for an underwater mining robot. In:15th international conference on control, automation and systems, pp 499–504Google Scholar
  37. 37.
    Yu X, Hung JY (2015) Coverage path planning based on a multiple sweep line decomposition. In: 41st annual conference of the IEEE industrial electronics society pp 4052–4458Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyLahorePakistan
  2. 2.Division of Advanced Mechanical EngineeringKangwon National UniversityChuncheonKorea
  3. 3.School of Electrical EngineeringKorea UniversitySeoulKorea

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