Skip to main content

Best path in mountain environment based on parallel A* algorithm and Apache Spark

Abstract

Pathfinding problem has several applications in our life and widely used in virtual environments. It has different goals such as shortest path, secure path, or optimal path. Pathfinding problem deals with a large amount of data since it considers every point located in 2D or 3D scenes. The number of possibilities in such a problem is huge. Moreover, it depends on determining standards of best path definition. In this paper, we introduce a parallel A* algorithm to find the optimal path using Apache Spark. The proposed algorithm is evaluated in terms of runtime, speedup, efficiency, and cost on a generated dataset with different sizes (small, medium, and large). The generated dataset considers real terrain challenges, such as the slope and obstacles. Hadoop Insight cluster provided by Azure has been used to run the application. The proposed algorithm reached a speedup up to 4.85 running on six worker nodes.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  1. 1.

    Ikeda T, Hsu M-Y, Imai H, Nishimura S, Shimoura H, Hashimoto T, Tenmoku K, Mitoh K (1994)A fast algorithm for finding better routes by AI search techniques. In: Proceedings of VNIS’94-1994 vehicle navigation and information systems conference. IEEE, pp 291–296

  2. 2.

    Jiang X, Lin Z, He T, Ma X, Ma S, Li S (2020) Optimal path finding with beetle antennae search algorithm by using ant colony optimization initialization and different searching strategies. IEEE Access 8:15459–15471

    Article  Google Scholar 

  3. 3.

    Kao M-Y, Reif JH, Tate SR (1996) Searching in an unknown environment: an optimal randomized algorithm for the cow-path problem. Inf Comput 131(1):63–79

    MathSciNet  Article  Google Scholar 

  4. 4.

    Salzman O, Stern R, (2020) Research challenges and opportunities in multi-agent path finding and multi-agent pickup and delivery problems. In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, pp 1711–1715

  5. 5.

    Alazzam H, Sharieh A (2018) Parallel DNA sequence approximate matching with multi-length sequence aware approach. Int J Comput Appl 975:8887

    Google Scholar 

  6. 6.

    AbuAlghanam O, Qatawneh M, Al Ofeishat HA, Adwan O, Huneiti A (2017) A new parallel matrix multiplication algorithm on tree-hypercube network using iman1 supercomputer. Int J Adv Comput Sci Appl 8(12):201–205

  7. 7.

    Zhang Y, Azad A, Buluç A (2020) Parallel algorithms for finding connected components using linear algebra. J Parallel Distrib Comput 144:14–27

    Article  Google Scholar 

  8. 8.

    Mezzoudj S, Behloul A, Seghir R, Saadna Y (2021) A parallel content-based image retrieval system using spark and tachyon frameworks. J King Saud Univ Comput Inf Sci 33(2):141–149

    Google Scholar 

  9. 9.

    Chen M, Mao S, Liu Y (2014) Big data: a survey. Mobile Netw Appl 19(2):171–209

    Article  Google Scholar 

  10. 10.

    Ashish DS, Munjal S, Mani M, Srivastava S (2021) Path finding algorithms. In: Emerging technologies in data mining and information security. Springer, pp 331–338

  11. 11.

    Satai HA, Zahra MMA, Rasool ZI, Abd-Ali RS, Pruncu CI (2021) Bézier curves-based optimal trajectory design for multirotor UAVs with any-angle pathfinding algorithms. Sensors 21(7):2460

    Article  Google Scholar 

  12. 12.

    Mandloi D, Arya R, Verma AK (2021) Unmanned aerial vehicle path planning based on A* algorithm and its variants in 3d environment. Int J Syst Assur Eng Manag 12:1–11

    Article  Google Scholar 

  13. 13.

    Foead D, Ghifari A, Kusuma MB, Hanafiah N, Gunawan E (2021) A systematic literature review of A* pathfinding. Procedia Comput Sci 179:507–514

    Article  Google Scholar 

  14. 14.

    Yiu YF, Mahapatra R (2020) Multi-agent pathfinding with hierarchical evolutionary hueristic a. In: 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE, pp 9–16

  15. 15.

    Brooks RA (1983) Solving the find-path problem by good representation of free space. IEEE Trans Syst Man Cybern 2:190–197

    MathSciNet  Article  Google Scholar 

  16. 16.

    Bonet B, Geffner H (2001) Planning as heuristic search. Artif Intell 129(1–2):5–33

    MathSciNet  Article  Google Scholar 

  17. 17.

    Mathew GE (2015) Direction based heuristic for pathfinding in video games. Procedia Comput Sci 47:262–271

    Article  Google Scholar 

  18. 18.

    Malathesh BC, Ibrahim FA, Nirisha PL, Kumar CN, Chand PK, Manjunatha N, Math SB, Thirthalli J, Manjappa AA, Parthasarathy R et al (2021) Embracing technology for capacity building in mental health: new path, newer challenges. Psychiatric Q 92(3):843–850

    Article  Google Scholar 

  19. 19.

    Spark A (2018) Apache spark

  20. 20.

    Laney D et al (2001) 3d data management: controlling data volume, velocity and variety. META Group Res arch Note 6(70):1

    Google Scholar 

  21. 21.

    Alnafessah A, Casale G (2020) Artificial neural networks based techniques for anomaly detection in apache spark. Cluster Comput 23(2):1345–1360

    Article  Google Scholar 

  22. 22.

    Sazaki Y, Satria H, Syahroyni M (2017) Comparison of A\(^*\)- and dynamic pathfinding algorithm with dynamic pathfinding algorithm for NPC on car racing game. In: 2017 11th International Conference on Telecommunication Systems Services and Applications (TSSA). IEEE, pp 1–6

  23. 23.

    Wan L, Zhang G, Li H, Li C (2021) A novel bearing fault diagnosis method using spark-based parallel ACO-k-means clustering algorithm. IEEE Access 9:28753–28768

    Article  Google Scholar 

  24. 24.

    Aziz K, Zaidouni D, Bellafkih M (2019) Leveraging resource management for efficient performance of apache spark. J Big Data 6(1):1–23

    Article  Google Scholar 

  25. 25.

    Rajita B, Ranjan Y, Umesh CT, Panda S (2020) Spark-based parallel method for prediction of events. Arab J Sci Eng 45(4):3437–3453

    Article  Google Scholar 

  26. 26.

    Mostafaeipour A, Jahangard Rafsanjani A, Ahmadi M, Arockia Dhanraj J, (2020) Investigating the performance of hadoop and spark platforms on machine learning algorithms. J Supercomput, pp 1–28

  27. 27.

    Jong D, Kwon I, Goo D, Lee D (2015) Safe pathfinding using abstract hierarchical graph and influence map. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, pp 860–865

  28. 28.

    Grenouilleau F, van Hoeve W-J, Hooker JN (2019) A multi-label A* algorithm for multi-agent pathfinding. Proc Int Conf Autom Plan Schedul 29:181–185

    Google Scholar 

  29. 29.

    Goldberg AV, Harrelson C (2005) Computing the shortest path: a search meets graph theory. In: SODA, vol 5. Citeseer, pp 156–165

  30. 30.

    Zhigalov K, Bataev DK, Klochkova E, Svirbutovich O, Ivashchenko G (2021) Problem solution of optimal pathfinding for the movement of vehicles over rough mountainous areas. In: IOP Conference Series: Materials Science and Engineering, vol 1111. IOP Publishing, p 012033

  31. 31.

    Josef S, Degani A (2020) Deep reinforcement learning for safe local planning of a ground vehicle in unknown rough terrain. IEEE Robot Autom Lett 5(4):6748–6755

    Article  Google Scholar 

  32. 32.

    Sinodkin A, Evdokimova T, Tiurikov M (2021) A method for constructing a global motion path and planning a route for a self-driving vehicle. In: IOP Conference Series: Materials Science and Engineering, vol 1086. IOP Publishing, p 012003

  33. 33.

    Masadeh R, Sharieh A, Jamal S, Qasem MH, Alsaaidah B (2020) Best path in mountain environment based on parallel hill climbing algorithm. Int J Adv Comput Sci Appl 11(9)

  34. 34.

    Montiel O, Sepúlveda R, Orozco-Rosas U (2015) Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field. J Intell Robot Syst 79(2):237–257

    Article  Google Scholar 

  35. 35.

    Rishiwal V, Yadav M, Arya K (2010) Finding optimal paths on terrain maps using ant colony algorithm. Int J Comput Theory Eng 2(3):416

    Article  Google Scholar 

  36. 36.

    Mocholi JA, Jaen J, Catala A, Navarro E (2010) An emotionally biased ant colony algorithm for pathfinding in games. Exp Syst Appl 37(7):4921–4927

    Article  Google Scholar 

  37. 37.

    Yao Y, Ni Q, Lv Q, Huang K (2015) A novel heterogeneous feature ant colony optimization and its application on robot path planning. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 522–528

  38. 38.

    Wang Z, Zhao Y, Liu Y, Lv C (2018) A speculative parallel simulated annealing algorithm based on apache spark. Concurr Comput Pract Exp 30(14):e4429

    Article  Google Scholar 

  39. 39.

    Phan T, Do P (2018) Improving the shortest path finding algorithm in apache spark graphx. In: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing, pp 67–71

  40. 40.

    Yang C-T, Chen T-Y, Kristiani E, Wu SF (2020) The implementation of data storage and analytics platform for big data lake of electricity usage with spark. J Supercomput, pp 1–26

  41. 41.

    Kang M, Lee J-G (2020) Effect of garbage collection in iterative algorithms on spark: an experimental analysis. J Supercomput, pp 1–15

  42. 42.

    Qin SJ, Chiang LH (2019) Advances and opportunities in machine learning for process data analytics. Comput Chem Eng 126:465–473

    Article  Google Scholar 

  43. 43.

    Sokolova M, Japkowicz N, Szpakowicz S (2006) Beyond accuracy, f-score and roc: a family of discriminant measures for performance evaluation. In: Australasian Joint Conference on Artificial Intelligence. Springer, pp 1015–1021

  44. 44.

    Nosrati M, Karimi R, Hasanvand HA (2012) Investigation of the*(star) search algorithms: characteristics, methods and approaches. World Appl Program 2(4):251–256

    Google Scholar 

  45. 45.

    Garling CT, Peter AH, Kochanek CS, Sand DJ, Crnojević D (2021) A search for satellite galaxies of nearby star-forming galaxies with resolved stars in lbt-song. arXiv preprint arXiv:2105.01082

  46. 46.

    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):100–107

    Article  Google Scholar 

  47. 47.

    Liu X, Gong D (2011) A comparative study of a-star algorithms for search and rescue in perfect maze. In: 2011 International Conference on Electric Information and Control Engineering. IEEE, pp 24–27

  48. 48.

    Candra A, Budiman MA, Hartanto K (2020) Dijkstra’s and a-star in finding the shortest path: a tutorial. In: 2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA). IEEE, pp 28–32

  49. 49.

    Besta M, Schneider M, Konieczny M, Cynk K, Henriksson E, Di Girolamo S, Singla A, Hoefler T (2020) Fatpaths: routing in supercomputers and data centers when shortest paths fall short. In: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, pp 1–18

  50. 50.

    Stan C-S, Pandelica A-E, Zamfir V-A, Stan R-G,  Negru C (2019) Apache spark and apache ignite performance analysis. In: 2019 22nd International Conference on Control Systems and Computer Science (CSCS). IEEE, pp 726–733

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hadeel Alazzam.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alazzam, H., AbuAlghanam, O. & Sharieh, A. Best path in mountain environment based on parallel A* algorithm and Apache Spark. J Supercomput (2021). https://doi.org/10.1007/s11227-021-04072-0

Download citation

Keywords

  • Apache Spark
  • Cluster
  • Parallel A* algorithm
  • Pathfinding problem