Table of Contents Author Guidelines Submit a Manuscript
International Journal of Computer Games Technology
Volume 2015, Article ID 736138, 11 pages
http://dx.doi.org/10.1155/2015/736138
Review Article

A Comprehensive Study on Pathfinding Techniques for Robotics and Video Games

Media and Games Innovation Centre of Excellence (MaGIC-X) UTM-IRDA Digital Media Centre, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

Received 30 September 2014; Accepted 18 March 2015

Academic Editor: Yiyu Cai

Copyright © 2015 Zeyad Abd Algfoor et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. R. Sturtevant and R. Geisberger, “A comparison of high-level approaches for speeding up pathfinding,” in Proceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE '10), pp. 76–82, October 2010. View at Scopus
  2. H. Kolivand and M. S. Sunar, “A survey of shadow volume algorithms in computer graphics,” IETE Technical Review, vol. 30, no. 1, pp. 38–46, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. J. van den Berg, R. Shah, A. Huang, and K. Goldberg, “ANA: anytime nonparametric A,” in Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI '11), pp. 105–111, August 2011. View at Scopus
  4. B. Bonet and H. Geffner, “Planning as heuristic search,” Artificial Intelligence, vol. 129, no. 1-2, pp. 5–33, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. M. Helmert, Understanding Planning Tasks: Domain Complexity and Heuristic Decomposition, vol. 4929 of Lecture Notes in Computer Science, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  6. A. Botea, B. Bouzy, M. Buro, C. Bauckhage, and D. S. Nau, “Pathfinding in games,” in Artificial and Computational Intelligence in Games, pp. 21–31, 2013. View at Google Scholar
  7. R. Graham, H. McCabe, and S. Sheridan, “Pathfinding in computer games,” ITB Journal, vol. 8, pp. 57–81, 2003. View at Google Scholar
  8. H. M. Choset, Principles of Robot Motion: Theory, Algorithms, and Implementation, MIT Press, Boston, Mass, USA, 2005.
  9. S. Russell and P. Norvig, “A modern approach,” in Artificial Intelligence, vol. 25, Prentice Hall, Englewood Cliffs, NJ, USA, 1995. View at Google Scholar
  10. P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100–107, 1968. View at Publisher · View at Google Scholar
  11. T. Ma, Q. Yan, W. Liu, D. Guan, and S. Lee, “Grid task scheduling: algorithm review,” IETE Technical Review, vol. 28, no. 2, pp. 158–167, 2011. View at Google Scholar · View at Scopus
  12. J. Carsten, A. Rankin, D. Ferguson, and A. Stentz, “Global planning on the mars exploration rovers: software integration and surface testing,” Journal of Field Robotics, vol. 26, no. 4, pp. 337–357, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. D. D. Harabor and A. Grastien, “Online graph pruning for pathfinding on grid maps,” in Proceedings of the 25th National Conference on Artificial Intelligence (AAAI '11), San Francisco, Calif, USA, 2011.
  14. N. R. Sturtevant, “Benchmarks for grid-based pathfinding,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 4, no. 2, pp. 144–148, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Uras, S. Koenig, and C. Hernández, “Subgoal graphs for optimal pathfinding in eight-neighbor grids,” in Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS '13), Rome, Italy, June 2013. View at Scopus
  16. D. Harabor and A. Grastien, “Improving jump point search,” in Proceedings of the 24th International Conference on Automated Planning and Scheduling, 2014.
  17. Z. Bnaya, R. Stern, A. Felner, R. Zivan, and S. Okamoto, “Multi-agent path finding for self interested agents,” in Proceedings of the 6th Annual Symposium on Combinatorial Search, pp. 38–46, July 2013. View at Scopus
  18. K. Anderson, “Additive heuristic for four-connected gridworlds,” in Proceedings of the 3rd Annual Symposium on Combinatorial Search, 2010.
  19. H. Jin, W. Wei, and L. Ziyan, “Multi-agent pathfinding system implemented on XNA,” in Proceedings of the 4th International Conference on Computational Intelligence and Communication Networks (CICN '12), pp. 651–655, Mathura, India, November 2012. View at Publisher · View at Google Scholar
  20. G. Sharon, N. R. Sturtevant, and A. Felner, “Online detection of dead states in real-time agent-centered search,” in Proceedings of the 6th Annual Symposium on Combinatorial Search, pp. 167–174, July 2013. View at Scopus
  21. S. Koenig and X. Sun, “Comparing real-time and incremental heuristic search for real-time situated agents,” Autonomous Agents and Multi-Agent Systems, vol. 18, no. 3, pp. 313–341, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Björnsson, M. Enzenberger, R. Holte, J. Schaeffer, and P. Yap, “Comparison of different grid abstractions for pathfinding on maps,” in Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI '03), pp. 1511–1512, Acapulco, Mexico, August 2003. View at Scopus
  23. H. J. Quijano and L. Garrido, “Improving cooperative robot exploration using an hexagonal world representation,” in Proceedings of the Electronics, Robotics and Automotive Mechanics Conference (CERMA '07), pp. 450–455, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. L. Chrpa and A. Komenda, “Smoothed hex-grid trajectory planning using helicopter dynamics,” in Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART '11), pp. 629–632, January 2011. View at Scopus
  25. L. Chrpa and P. Novák, “Dynamic trajectory replanning for unmanned aircrafts supporting tactical missions in urban environments,” in Holonic and Multi-Agent Systems for Manufacturing, pp. 256–265, Springer, 2011. View at Google Scholar
  26. M. F. Othman, M. Samadi, and M. H. Asl, “Simulation of dynamic path planning for real-time vision-base robots,” in Intelligent Robotics Systems: Inspiring the NEXT, pp. 1–10, Springer, 2013. View at Publisher · View at Google Scholar
  27. A. Stentz, “The focussed D* algorithm for real-time replanning,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI '95), vol. 2, pp. 1652–1659, 1995.
  28. D. Demyen and M. Buro, “Efficient triangulation-based pathfinding,” in Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, pp. 942–947, July 2006. View at Scopus
  29. M. Kapadia, K. Ninomiya, A. Shoulson, F. Garcia, and N. Badler, “Constraint-aware navigation in dynamic environments,” in Proceedings of the Motion on Games (MIG '13), pp. 111–120, 2013. View at Publisher · View at Google Scholar
  30. B. Nagy, “Cellular topology and topological coordinate systems on the hexagonal and on the triangular grids,” Annals of Mathematics and Artificial Intelligence, pp. 1–18, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. J. Carsten, D. Ferguson, and A. Stentz, “3D field D: improved path planning and replanning in three dimensions,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '06), pp. 3381–3386, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. B. Nagy, “Calculating distance with neighborhood sequences in the hexagonal grid,” in Combinatorial Image Analysis, pp. 98–109, Springer, 2005. View at Google Scholar
  33. V. Kiliç and M. E. Yalcin, “An active wave computing based path finding approach for 3-D environment,” in Proceedings of the IEEE International Symposium of Circuits and Systems (ISCAS '11), pp. 2165–2168, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. R. Yeniçeri and M. E. Yalçin, “Path planning on cellular nonlinear network using active wave computing technique,” in Bioengineered and Bioinspired Systems IV, vol. 7365 of Proceedings of SPIE, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. A. Nash and S. Koenig, “Any-angle path planning,” AI Magazine, vol. 34, no. 4, p. 9, 2013. View at Publisher · View at Google Scholar
  36. S. K. Ghosh, Visibility Algorithms in the Plane, Cambridge University Press, 2007. View at MathSciNet
  37. T. Lozano-Pérez and M. A. Wesley, “An algorithm for planning collision-free paths among polyhedral obstacles,” Communications of the ACM, vol. 22, no. 10, pp. 560–570, 1979. View at Publisher · View at Google Scholar · View at Scopus
  38. L. G. Shapiro and R. M. Haralick, “Decomposition of two-dimensional shapes by graph-theoretic clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 1, pp. 10–20, 1979. View at Publisher · View at Google Scholar · View at Scopus
  39. S. K. Ghosh and P. P. Goswami, “Unsolved problems in visibility graphs of points, segments, and polygons,” ACM Computing Surveys, vol. 46, article 22, 2013. View at Publisher · View at Google Scholar · View at Scopus
  40. M. Naderan-Tahan and T. Manzuri-Shalmani, “Efficient and safe path planning for a Mobile robot using genetic algorithm,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '09), pp. 2091–2097, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  41. D. Šišlák, P. Volf, and M. Pechoucek, “Accelerated A* trajectory planning: Grid-based path planning comparison,” in Proceedings of the 19th International Conference on Automated Planning & Scheduling (ICAPS '09), pp. 74–81, 2009.
  42. D. Šišlák, P. Volf, and M. Pechoucek, “Flight trajectory path planning,” in Proceedings of the Scheduling and Planning Applications Workshop, pp. 76–83, 2009.
  43. A. Nash, K. Daniel, S. Koenig, and A. Felner, “Theta*: any-angle path planning on grids,” in Proceedings of the National Conference on Artificial Intelligence, p. 1177, 2007.
  44. S. M. LaValle, Rapidly-Exploring Random Trees: A New Tool for Path Planning, 1998.
  45. M. Kallmann, “Navigation queries from triangular meshes,” in Motion in Games, vol. 6459 of Lecture Notes in Computer Science, pp. 230–241, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  46. M. Kapadia, A. Beacco, F. Garcia, V. Reddy, N. Pelechano, and N. I. Badler, “Multi-domain real-time planning in dynamic environments,” in Proceedings of the 12th ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA '13), pp. 115–124, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  47. D. Harabor and A. Botea, “Hierarchical path planning for multi-size agents in heterogeneous environments,” in Proceedings of the IEEE Symposium on Computational Intelligence and Games (CIG '08), pp. 258–265, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. S. Chen, G. Shi, and Y. Liu, “Fast path searching in real time 3D game,” in Proceedings of the WRI Global Congress on Intelligent Systems (GCIS '09), pp. 189–194, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  49. C. Niederberger, D. Radovic, and M. Gross, “Generic path planning for real-time applications,” in Proceedings of the Computer Graphics International (CGI '04), pp. 299–306, IEEE, Crete, Greece, June 2004. View at Publisher · View at Google Scholar · View at Scopus
  50. D. Ferguson and A. Stentz, “Using interpolation to improve path planning: the field D algorithm,” Journal of Field Robotics, vol. 23, pp. 79–101, 2006. View at Google Scholar
  51. H. Burchardt and R. Salomon, “Implementation of path planning using genetic algorithms on mobile robots,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '06), pp. 1831–1836, July 2006. View at Scopus
  52. K. Yang and S. Sukkarieh, “An analytical continuous-curvature path-smoothing algorithm,” IEEE Transactions on Robotics, vol. 26, no. 3, pp. 561–568, 2010. View at Publisher · View at Google Scholar · View at Scopus
  53. F. Lucas, C. Guettier, P. Siarry, A.-M. Milcent, and A. De La Fortelle, “Constrained navigation with mandatory waypoints in uncertain environment,” International Journal of Information Sciences and Computer Engineering, vol. 1, pp. 75–85, 2010. View at Google Scholar
  54. X. Cui and H. Shi, “A-based pathfinding in modern computer games,” International Journal of Computer Science and Network Security, vol. 11, pp. 125–130, 2011. View at Google Scholar
  55. A. Botea, M. Müller, and J. Schaeffer, “Near optimal hierarchical path-finding,” Journal of Game Development, vol. 1, pp. 7–28, 2004. View at Google Scholar
  56. R. E. Korf, “Optimal path-finding algorithms,” in Search in Artificial Intelligence, Symbolic Computation, pp. 223–267, Springer, New York, NY, USA, 1988. View at Publisher · View at Google Scholar · View at MathSciNet
  57. R. C. Coulter, “Implementation of the pure pursuit path tracking algorithm,” DTIC Document, 1992. View at Google Scholar
  58. Y. Cai and S. L. Goei, Simulations, Serious Games and Their Applications, Springer, 2014.
  59. M. G. Choi, J. Lee, and S. Y. Shin, “Planning biped locomotion using motion capture data and probabilistic roadmaps,” ACM Transactions on Graphics, vol. 22, no. 2, pp. 182–203, 2003. View at Publisher · View at Google Scholar · View at Scopus
  60. A. Kamphuis, M. Rook, and M. H. Overmars, “Tactical path finding in urban environments,” in Proceedings of the 1st International Workshop on Crowd Simulation, 2005.
  61. F. Rohrmuller, M. Althoff, D. Wollherr, and M. Buss, “Probabilistic mapping of dynamic obstacles using Markov chains for replanning in dynamic environments,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '08), pp. 2504–2510, Nice, France, September 2008. View at Publisher · View at Google Scholar
  62. R. A. Finkel and J. L. Bentley, “Quad trees a data structure for retrieval on composite keys,” Acta Informatica, vol. 4, no. 1, pp. 1–9, 1974. View at Publisher · View at Google Scholar · View at Scopus
  63. H. Samet, “Neighbor finding techniques for images represented by quadtrees,” Computer Graphics and Image Processing, vol. 18, no. 1, pp. 37–57, 1982. View at Publisher · View at Google Scholar · View at Scopus
  64. H. Samet, “An overview of quadtrees, octrees, and related hierarchical data structures,” in Theoretical Foundations of Computer Graphics and CAD, pp. 51–68, Springer, 1988. View at Google Scholar
  65. T. Reineking, C. Kohlhagen, and C. Zetzsche, “Efficient wayfinding in hierarchically regionalized spatial environments,” in Spatial Cognition VI. Learning, Reasoning, and Talking about Space, pp. 56–70, Springer, 2008. View at Google Scholar
  66. X. He, L. Chen, and Q. Zhu, “A novel method for large crowd flow,” in Transactions on Edutainment VI, vol. 6758 of Lecture Notes in Computer Science, pp. 67–78, Springer, Berlin, Germany, 2011. View at Publisher · View at Google Scholar
  67. J. Hirt, D. Gauggel, J. Hensler, M. Blaich, and O. Bittel, “Using quadtrees for realtime pathfinding in indoor environments,” in Research and Education in Robotics—EUROBOT 2010, vol. 156 of Communications in Computer and Information Science, pp. 72–78, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  68. A. Dooms, Parallel multi-agent path planning in dynamic environments for real-time applications [Ph.D. thesis], University Ghent, 2013.
  69. M. Naveed, D. E. Kitchin, and A. Crampton, Monte-Carlo Planning for Pathfinding in Real-Time Strategy Games, 2010.
  70. C. B. Browne, E. Powley, D. Whitehouse et al., “A survey of Monte Carlo tree search methods,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 4, no. 1, pp. 1–43, 2012. View at Publisher · View at Google Scholar · View at Scopus
  71. T. Cazenave, “Optimizations of data structures, heuristics and algorithms for path-finding on maps,” in Proceedings of the IEEE Symposium on Computational Intelligence and Games (CIG '06), pp. 27–33, Reno, Nev, USA, May 2006. View at Publisher · View at Google Scholar · View at Scopus
  72. D. Harabor and A. Botea, “Breaking path symmetries on 4-connected grid maps,” in Proceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE '10), pp. 33–38, October 2010. View at Scopus
  73. C. M. Potts and K. D. Krebsbach, “Iterative-expansion A,” in Proceedings of the 25th Florida Artificial Intelligence Research Society Conference (FLAIRS '12), Marco Island, Fla, USA, May 2012.
  74. S. Brand and R. Bidarra, “Multi-core scalable and efficient pathfinding with Parallel Ripple Search,” Computer Animation and Virtual Worlds, vol. 23, no. 2, pp. 73–85, 2012. View at Publisher · View at Google Scholar · View at Scopus