Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Path Planning for Autonomous Robot Navigation: Present Approaches

  • Conference paper
  • First Online:
Futuristic Trends in Network and Communication Technologies (FTNCT 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1396))

  • 607 Accesses

Abstract

An intelligent autonomous robot is in demand for robotic operations in the fields such as industry, medical, bionics, military. For any machine, designed to follow a precise sequence of instructions, self-positioning, path framing, map architecture, and obstacle prevention are the prerequisites of navigation. This paper presents a survey about the key navigation approaches explored by various authors in the last decade. The survey has a brief insight into the various approaches used for robot navigation concerning to the variable and invariable nature of the vicinity and the obstacle. The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of the discrete classical and heuristic approaches used by the researchers. The research assessment is finally concluded by aggregating the complete knowledge of the various path planning techniques by reviewing the literature.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Truong, X., Ngo, T.D.: Toward socially aware robot navigation in dynamic and crowded environments. IEEE Trans. Autom. Sci. Eng. 14, 18 (2017)

    Article  Google Scholar 

  2. Goyal, J.K.: A new approach of path planning for mobile robots, pp. 863–867 (2014)

    Google Scholar 

  3. Sharma, R., Sharma, A.: A review on interoperability and integration in smart homes. In: Singh, P.K., Sood, S., Kumar, Y., Paprzycki, M., Pljonkin, A., Hong, W.-C. (eds.) FTNCT 2019. CCIS, vol. 1206, pp. 116–128. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-4451-4_11

    Chapter  Google Scholar 

  4. Thoa, T., Copot, C., Trung, D., De Keyser, R.: Heuristic approaches in robot path planning: a survey. Rob. Auton. Syst. 86, 13–28 (2016)

    Article  Google Scholar 

  5. Kavraki, L.E., LaValle, S.M.: Motion planning. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 109–131. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-30301-5_6

  6. Papachristos, C., et al.: Autonomous exploration and inspection path planning for aerial robots using the robot operating system. In: Koubaa, A. (ed.) Robot Operating System (ROS). SCI, vol. 778, pp. 67–111. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91590-6_3

    Chapter  Google Scholar 

  7. Zhang, H., Lin, W., Chen, A.: Path planning for the mobile robot: a review. Symmetry (Basel) 10(10), 450 (2018)

    Article  Google Scholar 

  8. Sai, A., Haran, H.: A survey of autonomous mobile robot path planning approaches, pp. 27–29 (2017)

    Google Scholar 

  9. Raja, P.: Optimal path planning of mobile robots: a review. Int. J. Phys. Sci. 7(9), 1314–1320 (2012)

    Article  Google Scholar 

  10. Goerzen, C., Kong, Z., Mettler, B.: A survey of motion planning algorithms from the perspective of autonomous UAV guidance. J. Intell. Robot. Syst. Theory Appl. 57(1–4), 65–100 (2010)

    Article  Google Scholar 

  11. Wang, L.C., Yong, L.S., Ang, M.H.: Hybrid of global path planning and local navigation implemented on a mobile robot in indoor environment. In: IEEE International Symposium on Intelligent Control - Proceedings, pp. 821–826 (2002)

    Google Scholar 

  12. Atyabi, A., Powers, D.M.W.: Review of classical and heuristic-based navigation and path planning approaches. Int. J. Adv. Comput. Technol. 5, 1 (2013)

    Article  Google Scholar 

  13. Yang, L., Qi, J., Song, D., Xiao, J., Han, J., Xia, Y.: Survey of robot 3D path planning algorithms. J. Control Sci. Eng. (2016). https://www.hindawi.com/journals/jcse/2016/7426913/. Accessed 27 June 2020

  14. Hoy, M., Matveev, A.S., Savkin, A.V.: Algorithms for collision free navigation of mobile robots in complex cluttered environments: a survey. Robotica (2015). https://www.scopus.com/inward/record.uri/. Accessed 27 June 2020

  15. Patle, B.K., Babu L, G., Pandey, A., Parhi, D.R.K., Jagadeesh, A.: A review: on path planning strategies for navigation of mobile robot. Def. Technol. 15(4), 582–606 (2019)

    Google Scholar 

  16. Milos, S.: Roadmap methods vs. cell decomposition in robot motion planning. WSEAS (2007). https://www.semanticscholar.org/paper/Roadmap-methods-vs.-cell-decomposition-in-robot-Seda/. Accessed 27 June 2020

  17. Regli, W.: Robot Lab: robot path planning. Lecture notes of department of computer science. Drexel University. https://www.google.com/search?q=Regli+W.“RobotLabArobotpathplanning/. Accessed 27 June 2020

    Google Scholar 

  18. Schwartz, J.T., Sharir, M.: On the ‘piano movers’ problem I”. The case of a two‐dimensional rigid polygonal body moving amidst polygonal barriers. Commun. Pure Appl. Math. 36(3), 345–398 (1983)

    Google Scholar 

  19. Ajani, S.N., Amdani, S.Y.: Path planning techniques for navigation of mobile robot: a survey. IOSR J. Eng. 09(5), 77–84 (2019)

    Google Scholar 

  20. Latombe, J.-C.: Roadmap methods. In: Robot Motion Planning, pp. 153–199. Springer, Boston (1991). https://doi.org/10.1007/978-1-4615-4022-9_4

  21. Siméon, T., Laumond, J.-P., Nissoux, C.: Visibility-based probabilistic roadmaps for motion planning. Adv. Robot. 14(6), 477–493 (2000)

    Article  Google Scholar 

  22. Choset, H., et al.: Kavraki Lab | Principles of Robot Motion: Theory, Algorithms, and Implementation. MIT Press, Cambridge (2005). https://www.kavrakilab.org/publications/choset-burgard2005principles-of-robot.html. Accessed 06 July 2020

  23. Kim, J.: Workspace exploration and protection with multiple robots assisted by sensor networks. Int. J. Adv. Robot. Syst. 15(4) (2018)

    Google Scholar 

  24. Masehian, E., Amin-Naseri, M.R.: A Voronoi diagram-visibility graph-potencial field compound algorithm for robot path planning. J. Robot. Syst. 21(6), 275–300 (2004)

    Article  Google Scholar 

  25. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Rob. Res. 5(1), 90–98 (1986). https://doi.org/10.1177/027836498600500106

    Article  Google Scholar 

  26. Hwang, Y.K., Ahuja, N.: A potential field approach to path planning. IEEE Trans. Robot. Autom. 8(1), 23–32 (1992). https://doi.org/10.1109/70.127236

    Article  Google Scholar 

  27. Raja, R., Dutta, A., Venkatesh, K.S.: New potential field method for rough terrain path planning using genetic algorithm for a 6-wheel rover. Rob. Auton. Syst. 72, 295–306 (2015)

    Article  Google Scholar 

  28. Kuo, P.H., Li, T.H.S., Chen, G.Y., Ho, Y.F., Lin, C.J.: Migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller. Knowl. Eng. Rev. (2016). https://www.cambridge.org/core/journals/knowledge-engineering-review? Accessed 27 June 2020

  29. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

  30. Gul, F., et al.: A comprehensive study for robot navigation techniques. Cogent Eng. 6(1) (2019). Electrical & Electronic Engineering | Review Article

    Google Scholar 

  31. Carelli, R., Freire, E.O.: Corridor navigation and wall-following stable control for sonar-based mobile robots. Rob. Auton. Syst. 45(3–4), 235–247 (2003)

    Article  Google Scholar 

  32. Nazari Maryam Abadi, D., Khooban, M.H.: Design of optimal Mamdani-type fuzzy controller for nonholonomic wheeled mobile robots. J. King Saud Univ. Eng. Sci. 27(1), 92–100 (2015)

    Google Scholar 

  33. Castillo, O., Neyoy, H., Soria, J., García, M., Valdez, F.: Dynamic fuzzy logic parameter tuning for ACO and its application in the fuzzy logic control of an autonomous mobile robot. Int. J. Adv. Robot. Syst. 10(1) (2013)

    Google Scholar 

  34. Odry, Á., Fullér, R., Rudas, I.J., Odry, P.: Fuzzy control of self-balancing robots: a control laboratory project. Comput. Appl. Eng. Educ. 28(3), 512–535 (2020). https://doi.org/10.1002/cae.22219

    Article  Google Scholar 

  35. Singh, Y.V., Kumar, B., Chand, S., Sharma, D.: A hybrid approach for requirements prioritization using logarithmic fuzzy trapezoidal approach (LFTA) and artificial neural network (ANN). In: Singh, P.K., Paprzycki, M., Bhargava, B., Chhabra, J.K., Kaushal, N.C., Kumar, Y. (eds.) FTNCT 2018. CCIS, vol. 958, pp. 350–364. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-3804-5_26

    Chapter  Google Scholar 

  36. Chen, J.: Neural Network Definition. Investopedia (2020)

    Google Scholar 

  37. Janglova, D.: Neural Networks in Mobile Robot Motion, December 2004

    Google Scholar 

  38. Pothal, J.K., Parhi, D.R.: Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system. Rob. Auton. Syst. 72, 48–58 (2015)

    Article  Google Scholar 

  39. Eberhart, R., Kennedy, J.: New optimizer using particle swarm theory. In: Proceedings of the International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)

    Google Scholar 

  40. Tang, Q., Eberhard, P.: Cooperative motion of swarm mobile robots based on particle swarm optimization and multibody system dynamics. Mechanics Based Design of Structures and Machines 39(2), 179–193 (2011)

    Article  Google Scholar 

  41. Chen, Y.L., Cheng, J., Lin, C., Wu, X., Ou, Y., Xu, Y.: Classification-based learning by particle swarm optimization for wall-following robot navigation. Neurocomputing 113, 27–35 (2013)

    Article  Google Scholar 

  42. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  43. Tan, G.Z., He, H., Sloman, A.: Ant colony system algorithm for real-time globally optimal path planning of mobile robots. Zidonghua Xuebao/Acta Autom. Sin. 33(3), 279–285 (2007)

    Article  Google Scholar 

  44. Liu, J., Yang, J., Liu, H., Tian, X., Gao, M.: An improved ant colony algorithm for robot path planning. Soft. Comput. 21(19), 5829–5839 (2016). https://doi.org/10.1007/s00500-016-2161-7

    Article  Google Scholar 

  45. Purian, F.K., Sadeghian, E.: Mobile robots path planning using ant colony optimization and Fuzzy Logic algorithms in unknown dynamic environments. In: CARE 2013 - 2013 IEEE International Conference on Control, Automation, Robotics and Embedded Systems, Proceedings (2013)

    Google Scholar 

  46. Liu, L.Q.: Path planning of underwater vehicle in 3D space based on ant colony algorithm (2008). https://www.researchgate.net/publication/291078044. Accessed 06 July 2020

  47. Castillo, O., Neyoy, H., Soria, J., Melin, P., Valdez, F.: A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot. Appl. Soft Comput. J. 28, 150–159 (2015)

    Article  Google Scholar 

  48. Bremermann, H.: The evolution of intelligence: the nervous system as a model of its environment. Department of Mathematics, University of Washington, Seattle, Washington (1958)

    Google Scholar 

  49. Holland, J.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992). https://ieeexplore.ieee.org/book/6267401. Accessed 06 July 2020

  50. Xiao, J., Michalewicz, Z., Zhang, L., Trojanowski, K.: Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans. Evol. Comput. 1(1), 18–28 (1997)

    Article  Google Scholar 

  51. Kala, R.: Coordination in navigation of multiple mobile robots. Cybern. Syst. 45(1), 1–24 (2014)

    Article  MathSciNet  Google Scholar 

  52. Shi, P., Cui, Y.: Dynamic path planning for mobile robot based on genetic algorithm in unknown environment. In: 2010 Chinese Control and Decision Conference, CCDC 2010, pp. 4325–4329 (2010)

    Google Scholar 

  53. Creaser, P.A., Stacey, B.A., White, B.A.: Evolutionary generation of fuzzy guidance laws. In: IEE Conference Publication, no. 455, pp. 883–888, February 1998

    Google Scholar 

  54. Lin, K.P., Hung, K.C.: An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making. Knowl. Based Syst. 24(6), 877–889 (2011)

    Article  Google Scholar 

  55. Ni, J., Wang, K., Huang, H., Wu, L., Luo, C.: Robot path planning based on an improved genetic algorithm with variable length chromosome. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 145–149, August 2016

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, IT Central University of Jammu, Jammu, 181143, India

    Shagun Verma

  2. Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Budapest, Hungary

    Neerendra Kumar

Authors
  1. Shagun Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neerendra Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shagun Verma .

Editor information

Editors and Affiliations

  1. ABES Engineering College, Ghaziabad, India

    Pradeep Kumar Singh

  2. Southern Federal University, Rostov-on-Don, Russia

    Gennady Veselov

  3. Southern Federal University, Rostov-on-Don, Russia

    Anton Pljonkin

  4. Jaypee Institute of Information Technology, Waknaghat, India

    Yugal Kumar

  5. Systems Research Institute, Warszawa, Poland

    Marcin Paprzycki

  6. Southern Federal University, Rostov-on-Don, Russia

    Yuri Zachinyaev

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Verma, S., Kumar, N. (2021). Path Planning for Autonomous Robot Navigation: Present Approaches. In: Singh, P.K., Veselov, G., Pljonkin, A., Kumar, Y., Paprzycki, M., Zachinyaev, Y. (eds) Futuristic Trends in Network and Communication Technologies. FTNCT 2020. Communications in Computer and Information Science, vol 1396. Springer, Singapore. https://doi.org/10.1007/978-981-16-1483-5_25

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-1483-5_25

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1482-8

  • Online ISBN: 978-981-16-1483-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation