Skip to main content
SpringerLink
Log in

Mobile Robot Path Planning Using a Flower Pollination Algorithm-Based Approach

  • Chapter
  • First Online:
Nature-Inspired Computation in Navigation and Routing Problems

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Considering the growing role of mobile robots in our everyday life and in various other applications, robust and safe navigation for these mobile robots is of utmost importance. Though the computational power of the computers has increased, there is still a need for the robust algorithms which are that can yield the most optimal, safe and least energy-demanding path. Hence, in this chapter, first, the classification of different approaches used for robot path planning has been briefly discussed, followed by the discussion of several soft computing-based approaches and the challenges associated with them. Then, a flower pollination algorithm-based approach has been discussed for mobile robot path planning. Several criteria like distance covered by the robot, two-layered safety and the number of turns required during the path traversal are used to evaluate the path for its optimality. The result of the algorithm is given using different examples.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Abbas NH, Saleh BJ (2016) Design of a kinematic neural enhanced hybrid firefly for mobile robots based on enhanced hybrid firefly-artificial bee colony algorithm. Al-Khwarizmi Eng J 12(1):45–60

    Google Scholar 

  2. Abbas NH, Abdulsaheb JA (2017) An adaptive multi-objective artificial bee colony algorithm for multi-robot path planning. Assoc Arab Univ J Eng Sci 24(3):168–189

    Google Scholar 

  3. Abramowitz M, Stegun IA (1964) Handbook of mathematical functions with formulas, graphs, and mathematical tables. National Bureau of Standards Applied Mathematics Series, USA

    Google Scholar 

  4. Alotaibi ETS, Al-Rawi H (2018) A complete multi-robot path-planning algorithm. Auton Agent Multi-Agent Syst 32(5):693–740

    Article  Google Scholar 

  5. Atyabi A, Phon-Amnuaisuk S, Ho CK (2010) Applying area extension PSO in robotic swarm. J Intell Robot Syst 58:253–285

    Article  MATH  Google Scholar 

  6. Baxter JL, Burke EK, Garibald JM, Norman M (2009) Shared potential fields and their place in a multi-robot co-ordination taxonomy. Robot Auton Syst 57(10):1048–1055

    Article  Google Scholar 

  7. Bhagade AS, Puranik PV (2012) Artificial bee colony (ABC) algorithm for vehicle routing optimisation problem. Int J Soft Comput Eng 2(2):329–333

    Google Scholar 

  8. Bhattacharjee P, Rakshit P, Goswami I, Konar A, Nagar AK (2011) Multi-robot path-planning using artificial bee colony optimisation algorithm. In: 2011 Third world congress on nature and biologically inspired computing. IEEE, pp 219–224

    Google Scholar 

  9. Brand M, Yu X-H (2013) Autonomous robot path optimisation using firefly algorithm. In: International conference on machine learning and cybernetics, vol 3. Tianjin, pp 14–17

    Google Scholar 

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

    Article  Google Scholar 

  11. Chakraborty J, Konar A, Jain LC, Chakraborty UK (2009) Cooperative multi-robot path planning using differential evolution. J Intell Fuzzy Syst 20(1, 2):13–27

    Article  MATH  Google Scholar 

  12. Christensen AL, Rehan OG, Dorigo M (2008) Synchronization and fault detection in autonomous robots. In: IEEE/RSJ intelligent conference on robots and systems, pp 4139–4140

    Google Scholar 

  13. Contreras-Cruz MA, Ayala-Ramirez V, Hernandez-Belmonte UH (2015) Mobile robot path planning using artificial bee colony and evolutionary programming. Appl Soft Comput 30:319–328

    Article  Google Scholar 

  14. Couceiro MS, Rocha RP, Nuno F (2013) A PSO multi-robot exploration approach over unreliable MANETs. Adv Robot 27(16):1221–1234

    Article  Google Scholar 

  15. Das PK, Sahoo BM, Behera HS, Vashisht S (2016a) An improved particle swarm optimisation for multi-robot path planning. In: 2016 international conference on innovation and challenges in cyber security (ICICCS-INBUSH). IEEE, pp 97–106

    Google Scholar 

  16. Das PK, Behera HS, Panigrahi BK (2016) A hybridization of an improved particle swarm optimisation and gravitational search algorithm for multi-robot path planning. Swarm Evol Comput 28:14–28

    Article  Google Scholar 

  17. Dorigo M (1992) Optimisation, learning and natural algorithms. Thesis, Department of Electronics, Politecnico di Milano, Italy

    Google Scholar 

  18. Elshamli A, Abdullah HA, Areibi S (2004) Genetic algorithm for dynamic path planning. In: Canadian conference on electrical and computer engineering 2004 (IEEE Cat. No. 04CH37513), vol 2, pp 677–680

    Google Scholar 

  19. Faridi AQ, Sharma S, Shukla A, Tiwari R, Dhar J (2018) Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment. Intel Serv Robot 11(2):171–186

    Article  Google Scholar 

  20. Ge SS (2006) Autonomous mobile robots: sensing, control, decision making and applications. CRC press

    Google Scholar 

  21. Gemeinder M, Gerke M (2003) GA-based path planning for mobile robot systems employing an active search algorithm. Appl Soft Comput 3:149–158

    Article  Google Scholar 

  22. Guan-Zheng T, Huan HE, Aaron S (2007) Ant colony system algorithm for real time globally optimal path planning of mobile robots. Acta Autom Sin 33(3):279–285

    Article  MATH  Google Scholar 

  23. Hachour O (2008) Path planning of autonomous mobile robot. Int J Syst Appl Eng Dev 2(4):178–190

    Google Scholar 

  24. Hidalgo-Paniagua A, Vegae Rodriguez MA, Ferruz J, Pavon N (2015) Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach. Soft Comput. 1–16

    Google Scholar 

  25. Hong Q, Ke X, Alexander T (2013) An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing 120:509–517

    Article  Google Scholar 

  26. Holland J (1975) Adaptation in natural and artificial systems. MIT Press, Cambridge, MA, USA

    Google Scholar 

  27. Jianjun N, Wang K, Huang H, Wu L, Luo C (2016) Robot path planning based on an improved genetic algorithm with variable length chromosome. In: 12th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD). https://doi.org/10.1109/FSKD.2016.7603165

  28. Kala R (2012) Multi-robot path planning using co-evolutionary genetic programming. Expert Syst Appl 39(3):3817–3831

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  30. Kang X, Yue Y, Li D, Maple C (2011) Genetic algorithm based solution to dead problems in robot navigation. Int J Comput Appl Technol 41(3–4):177–184

    Article  Google Scholar 

  31. Karaboga D (2005) An idea based on honey bee swarm for numerical optimisation. Erciyes University, Engineering Faculty, Computer Engineering Department. Technical report-tr06

    Google Scholar 

  32. Kavraki LE, Kolountzakis MN, Latombe JC (1998) Analysis of probabilistic roadmaps for path planning. IEEE Trans Robot Autom 14(1):166–171

    Article  Google Scholar 

  33. Kennedy J, Eberhart R (1995) Particle swarm optimisation. In: Proceedings of ICNN’95—international conference on neural networks, Perth, WA, Australia, vol 4, pp 1942–1948. https://doi.org/10.1109/icnn.1995.488968

  34. Khatib O (1985) Real-time obstacle avoidance for manipulators and mobile robots. In: Paper presented at the 1985 IEEE international conference on robotics and automation, St. Louis, MO

    Google Scholar 

  35. Kumar PB, Sahu C, Parhi DR (2018) A hybridised regression-adaptive ant colony optimisation approach for navigation of humanoids in a cluttered environment. Appl Soft Comput 68:565–585

    Article  Google Scholar 

  36. Lamini C, Benhlima S, Elbekri A (2018) Genetic algorithm based approach for autonomous mobile robot path planning. Procedia Comput Sci 127:180–189. https://doi.org/10.1016/j.procs.2018.01.113

    Article  Google Scholar 

  37. Li G, Chou W (2018) Path planning for mobile robot using self-adaptive learning particle swarm optimisation. Sci China Inf Sci 61(5):052204

    Article  MathSciNet  Google Scholar 

  38. Liang JH, Lee CH (2015) Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm. Adv Eng Softw 79:47–56

    Article  Google Scholar 

  39. Liu F, Liang S, Xian X (2014) Optimal robot path planning for multiple goals visiting based on tailored genetic algorithm. Int J Comput Intell Syst 7(6):1109–1122

    Article  Google Scholar 

  40. Liu J, Yang J, Liu H, Tian X, Gao M (2017) An improved ant colony algorithm for robot path planning. Soft Comput 21(19):5829–5839

    Article  Google Scholar 

  41. Liu S, Mao L, Yu J (2006) Path planning based on ant colony algorithm and distributed local navigation for multi-robot systems. In: Proceedings of the 2006 IEEE international conference of mechatronics and automation, 1733–1738

    Google Scholar 

  42. Ma Q, Lei X (2010) Dynamic path planning of mobile robots based on ABC algorithm. In: International conference on artificial intelligence and computational intelligence. Springer, Berlin, , pp 267–274

    Chapter  Google Scholar 

  43. Mitic M, Miljkovic Z (2015) Bio-inspired approach to learning robot motion trajectories and visual control commands. Expert Syst Appl 42:2624–2637. https://doi.org/10.1016/j.eswa.2014.10.053

    Article  Google Scholar 

  44. Mohanty PK, Parhi DR (2015) A new hybrid optimisation algorithm for multiple mobile robots navigation based on the CS-ANFIS approach. Memetic Comput 7(4):255–273

    Article  Google Scholar 

  45. Patle BK, Parhi DRK, Jagadeesh A, Kashyap SK (2018) Matrix-Binary Codes based Genetic Algorithm for path planning of mobile robot. Comput Electr Eng 67:708–728

    Article  Google Scholar 

  46. Patle BK, Ganesh LB, Pandey A, Parhi DRK, Jagadeesh A (2019) A review: on path planning strategies for navigation of mobile robot. Defence Technol 15(4):582–606. https://doi.org/10.1016/j.dt.2019.04.011

    Article  Google Scholar 

  47. Potter MA, Jong KAD (1994) A cooperative co-evolutionary approach to function optimisation. In: Davidor Y, Schwefel HP, Männer R (eds) Proceedings of the third conference on parallel problem solving from nature. Springer, Berlin, pp 249–257

    Google Scholar 

  48. Potter MA, Jong KAD (2000) Cooperative coevolution: an architecture for evolving co-adapted subcomponents. Evol Comput 8(1):1–29

    Article  Google Scholar 

  49. Pradhan SK, Parhi DR, Panda AK, Behera RK (2006) Potential field method to navigate several mobile robots. Appl Intell 25(3):321–333

    Article  Google Scholar 

  50. Purian FK, Sadeghian E (2013) Mobile robots path planning using ant colony optimisation and fuzzy logic algorithm in unknown dynamic environment. In: International conference on control, automation, robotics and embedded systems (CARE), pp 1–6

    Google Scholar 

  51. Qiongbing Z, Lixin D (2016) A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimisation problems. Expert Syst Appl 60:183–189

    Article  Google Scholar 

  52. Rakshit P, Konar A, Bhowmik P, Goswami I, Das S, Jain LC, Nagar AK (2013) Realization of an adaptive memetic algorithm using differential evolution and q-learning: a case study in multi-robot path planning. IEEE Trans Syst Man Cybern Syst 43(4):814–831

    Article  Google Scholar 

  53. Raja P, Pugazhenthi S (2009) Path planning for mobile robots in dynamic environments using particle swarm optimisation. In 2009 IEEE international conference on advances in recent technologies in communication and computing, pp 401–405

    Google Scholar 

  54. Rajput U, Kumari M (2017) Mobile robot path planning with modified ant colony optimisation. Int J Bio-Inspired Comput 9(2):106–113

    Article  Google Scholar 

  55. Sadhu AK, Konar A, Bhattacharjee T, Das S (2018) Synergism of firefly algorithm and Q-learning for robot arm path planning. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2018.03.014

    Article  Google Scholar 

  56. Saffari MH, Mahjoob MJ (2009) Bee colony algorithm for real-time optimal path planning of mobile robots. In: Soft computing, computing with words and perceptions in system analysis. Decision and control, pp 1–4

    Google Scholar 

  57. Sánchez-Ante G, Latombe JC (2002) Using a PRM planner to compare centralised and decoupled planning for multi-robot systems. In: Proceedings of IEEE international conference on robotics and automation, Washington, DC

    Google Scholar 

  58. Shi P, Cui Y (2010) Dynamic path planning for mobile robot based on genetic algorithm in unknown environment. In: Proceedings of the Chinese control and decision conference, Xuzhou, China, pp 4325–4329

    Google Scholar 

  59. Shing MT, Parkar GB (1993) Genetic algorithm for the development of real-time multi-heuristic search strategies. In: Proceedings 5th conference on genetic algorithm. Los Aitos, California: Morgan Kaumann Publication, pp 565–570

    Google Scholar 

  60. Sutantyo D, Levi P, Moslinger C, Read M (2013) Collective-adaptive levy flight for underwater multi-robot exploration. In: International conference on mechatronics and automation, pp 456–462

    Google Scholar 

  61. Sutantyo D, Levi P (2015) Decentralised underwater multi robot communication using bio-inspired approaches. Artif Life Robot 20:152–158

    Article  Google Scholar 

  62. Selekwa MF, Dunlap DD, Shi D, Collins EGJ (2008) Robot navigation in very cluttered environments by preference-based fuzzy behaviours. Robot Auton Syst 56(3):231–246

    Article  Google Scholar 

  63. Tang Q, Eberhard P (2011) Cooperative motion of swarm mobile robots based on particle swarm optimisation and multibody system dynamics. Mech Base Des Struct Mach 39(2):179–193

    Article  Google Scholar 

  64. Tang X, Li L, Jiang B (2014) Mobile robot SLAM method based on multi-agent particle swarm optimised particle filter. J China Univ Posts Telecommun 21(6):78–86

    Article  Google Scholar 

  65. Thabit S, Mohades A (2018) Multi-robot path planning based on multi-objective particle swarm optimisation. IEEE Access 7:2138–2147

    Article  Google Scholar 

  66. Wang G, Guo L, Duan H, Wang H, Liu L, Shao MA (2012a) Hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning. Sci World J 583973. https://doi.org/10.1100/2012/583973

    Google Scholar 

  67. Wang G, Guo L, Hong D, Duan H, Liu L, Wang HA (2012b) Modified firefly algorithm for UCAV path planning. Int J Hosp Inf Technol 5(3):123e44

    Google Scholar 

  68. Xiao J, Michalewicz Z, Zhang L, Trojanowski K (1997) Adaptive evolutionary planner/ navigator for mobile robot. IEEE Trans Evol Comput 1(1)

    Google Scholar 

  69. Xuan VH, Cheolkeun H, Jewon L (2013) Novel hybrid optimisation algorithm using PSO and MADS for the trajectory estimation of a four track wheel skid-steered mobile robot. Adv Robot 27(18):1421–1437

    Article  Google Scholar 

  70. Yang SX, Hu Y, Meng MQ (2006) A knowledge based GA path planning of multiple mobile robots in dynamic environments. In: 2006 IEEE conference on robotics: automation and Mechatronics, Bangkok, Thailand, pp 1–6

    Google Scholar 

  71. Yang XS (2008) Nature-inspired metaheuristic algorithm. Luniver press

    Google Scholar 

  72. Yang XS (2012) Flower pollination algorithms. In: Yang XS (ed) Nature-inspired optimisation algorithms. Elsevier, London, pp 155–173

    Google Scholar 

  73. Yang XS, Deb S (2009) Cuckoo search via Levy flights. In: Nature and biologically inspired computing, NaBIC 2009, IEEE World Congress, pp 210–214

    Google Scholar 

  74. Yang XS, Karamanoglu M (2013) Swarm intelligence and bio-inspired computation: an overview. In: Swarm intelligence and bio-inspired computation. Elsevier, pp. 3–23

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Atul Mishra & Sankha Deb

Authors
  1. Atul Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sankha Deb
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Atul Mishra .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  2. College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China

    Yu-Xin Zhao

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mishra, A., Deb, S. (2020). Mobile Robot Path Planning Using a Flower Pollination Algorithm-Based Approach. In: Yang, XS., Zhao, YX. (eds) Nature-Inspired Computation in Navigation and Routing Problems. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-1842-3_6

Download citation

Publish with us

Policies and ethics

Search

Navigation