Cellular Robotic Ants Synergy Coordination for Path Planning

  • Konstantinos IoannidisEmail author
  • Georgios Ch. Sirakoulis
  • Ioannis Andreadis
Part of the Emergence, Complexity and Computation book series (ECC, volume 13)


In this chapter, a unified architecture is proposed for a robot team in order to accomplish several tasks based on the application of an enhanced Cellular Automata (CA) path planner. The presented path planner can produce adequate collision-free pathways with minimum hardware resources and low complexity levels. During the course of a robot team to its final destination, dynamic obstacles are detected and avoided in real time as well as coordinated movements are executed by applying cooperations in order to maintain the team’s initial formation. The inherit parallelism and simplicity of CA result in a path planner that requires low computational resources and thus, its implementation in miniature robots is straightforward. Cooperations are limited to a minimum so that further resource reduction can be achieved. For this purpose, the basic fundamentals of another artificial intelligence method, namely Ant Colonies Optimization (ACO) technique, were applied. The entire robot team is divided into equally numbered subgroups and an ACO algorithm is applied to reduce the complexity. As each robot moves towards to its final position, it creates a trail of an evaporated substance, called “pheromone”. The “pheromone” and its quantity are detected by the following robots and thus, every robot is absolved by the necessity of continuous communication with its neighbors. The total complexity of the presented architecture results to a possible implementation using a team of miniature robots where all available resources are exploited.


Path Planning Cellular Automaton Obstacle Avoidance Path Planner Path Planning Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Akst, J.: Send in the bots. Scientist 27(10), 45 (2013) (Cited By since (1996))Google Scholar
  2. 2.
    Arney, T.: Dynamic path planning and execution using b-splines. In: Third International Conference on Information and Automation for Sustainability, ICIAFS 2007. pp. 1–6 (2007)Google Scholar
  3. 3.
    Barnes, L., Garcia, R., Fields, M.A., Valavanis, K.: Swarm formation control utilizing ground and aerial unmanned systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 4205–4205 (2008)Google Scholar
  4. 4.
    Beckers, R., Deneubourg, J.L., Goss, S.: Trails and u-turns in the selection of a path by the ant lasius niger. J. Theor. Biol. 159, 397–415 (1992)Google Scholar
  5. 5.
    Belkhous, S., Azzouz, A., Saad, M., Nerguizian, C., Nerguizian, V.: A novel approach for mobile robot navigation with dynamic obstacles avoidance. J. Intell. Robotics Syst. 44(3), 187–201 (2005)CrossRefGoogle Scholar
  6. 6.
    Blum, C.: Ant colony optimization: Introduction and recent trends. Physics of Life Reviews 2(4), 353–373 (2005)CrossRefGoogle Scholar
  7. 7.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406, 39–42 (2000)CrossRefGoogle Scholar
  8. 8.
    Bonani, M., Raemy, X., Pugh, J., Mondana, F., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.C., Floreano, D., Martinoli, A.: The e-puck, a Robot Designed for Education in Engineering. In: Proceedings of the 9th Conference on Autnomous Robot Systems and Competitions, vol. 1, pp. 59–65 (2009)Google Scholar
  9. 9.
    Brown, M., Lowe, D.: Automatic panoramic image stitching using invariant features. International Journal of Computer Vision 74(1), 59–73 (2007)CrossRefGoogle Scholar
  10. 10.
    Burgard, W., Moors, M., Stachniss, C., Schneider, F.: Coordinated multi-robot exploration. IEEE Trans. Robot. 21(3), 376–386 (2005)CrossRefGoogle Scholar
  11. 11.
    Charalampous, K., Amanatiadis, A., Gasteratos, A.: Efficient robot path planning in the presence of dynamically expanding obstacles. In: Sirakoulis, G., Bandini, S. (eds.) Cellular Automata. Lecture Notes in Computer Science, vol. 7495, pp. 330–339. Springer, Berlin Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Charalampous, K., Kostavelis, I., Amanatiadis, A., Gasteratos, A.: Real-time robot path planning for dynamic obstacle avoidance. J. Cell. Automata Appear (2014)Google Scholar
  13. 13.
    Chen, M.J., Huang, C.H., Lee, W.L.: A fast edge-oriented algorithm for image interpolation. Image Vis. Comput. 23(9), 791–798 (2005)CrossRefGoogle Scholar
  14. 14.
    Chicco, G., Ionel, O.M., Porumb, R.: Electrical load pattern grouping based on centroid model with ant colony clustering. IEEE Trans. Power Syst. 28(2), 1706–1715 (2013)CrossRefGoogle Scholar
  15. 15.
    Conti, C., Roisenberg, M., Neto, G., Porsani, M.: Fast seismic inversion methods using ant colony optimization algorithm. IEEE Geosci. Remote Sens. Lett. 10(5), 1119–1123 (2013)CrossRefGoogle Scholar
  16. 16.
    Daniel, K., Nash, A., Koenig, S., Felner, A.: Theta*: any-angle path planning on grids. J. Artif. Intell. Res. 39, 533–579 (2010)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Dechter, R., Pearl, J.: Generalized best-first search strategies and the optimality of a*. J. ACM 32(3), 505–536 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Defoort, M., Floquet, T., Kokosy, A., Perruquetti, W.: Sliding-mode formation control for cooperative autonomous mobile robots. IEEE Trans. Ind. Electron. 55(11), 3944–3953 (2008)CrossRefGoogle Scholar
  19. 19.
    Deneubourg, J., Goss, S.: Collective patterns and decision-making. Ethol. Ecol. Evol. 1(4), 295–311 (1989)CrossRefGoogle Scholar
  20. 20.
    Dhiman, N.K., Deodhare, D., Khemani, D.: A review of path planning and mapping technologies for autonomous mobile robot systems. In: Proceedings of the 5th ACM COMPUTE Conference: Intelligent and Scalable System Technologies, COMPUTE ’12, pp. 3:1–3:8. ACM, New York, NY, USA (2012)Google Scholar
  21. 21.
    Di Caro, G., Dorigo, M.: Antnet: Distributed stigmergetic control for communications networks. J. Artif. Int. Res. 9(1), 317–365 (1998)zbMATHGoogle Scholar
  22. 22.
    Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Italy (1992)Google Scholar
  23. 23.
    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)CrossRefGoogle Scholar
  24. 24.
    Du, Z., Qu, D., Xu, F., Xu, D.: A hybrid approach for mobile robot path planning in dynamic environments. In: IEEE International Conference on Robotics and Biomimetics, ROBIO 2007, pp. 1058–1063 (2007)Google Scholar
  25. 25.
    Fax, J., Murray, R.: Information flow and cooperative control of vehicle formations. IEEE Trans. Autom. Control 49(9), 1465–1476 (2004)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Fredslund, J., Mataric, M.: A general algorithm for robot formations using local sensing and minimal communication. IEEE Trans. Robot. Autom. 18(5), 837–846 (2002)CrossRefGoogle Scholar
  27. 27.
    Garnier, S., Combe, M., Jost, C., Theraulaz, G.: Do Ants Need to estimate the geometrical properties of trail bifurcations to find an efficient route? A swarm robotics test Bed. PLoS Comput. Biol. 9(3), e1002,903\(+\) (2013)Google Scholar
  28. 28.
    Garnier, S., Gurcheau, A., Combe, M., Fourcassi, V., Theraulaz, G.: Path selection and foraging efficiency in argentine ant transport networks. Behav. Ecol. Sociobiol. 63(8), 1167–1179 (2009)CrossRefGoogle Scholar
  29. 29.
    Garnier, S., Tache, F., Combe, M., Grimal, A., Theraulaz, G.: Alice in pheromone land: An experimental setup for the study of ant-like robots. In: IEEE Swarm Intelligence Symposium, SIS 2007, pp. 37–44 (2007)Google Scholar
  30. 30.
    Ge, S.S., Fua, C.H.: Queues and artificial potential trenches for multirobot formations. IEEE Trans. Robot. 21(4), 646–656 (2005)CrossRefGoogle Scholar
  31. 31.
    Georgoudas, I., Sirakoulis, G., Scordilis, E., Andreadis, I.: A cellular automaton simulation tool for modelling seismicity in the region of xanthi. Environ. Model. Softw. 22(10), 1455–1464 (2007)CrossRefGoogle Scholar
  32. 32.
    Goss, S., Beckers, R., Deneubourg, J., Aron, S., Pasteels, J.: How trail laying and trail following can solve foraging problems for ant colonies. In: Hughes, R. (ed.) Behav.ural Mechanisms of Food Selection, NATO ASI Series, vol. 20, pp. 661–678. Springer, Berlin (1990)CrossRefGoogle Scholar
  33. 33.
    Grassé, P.P.: La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d’interprétation du comportement des termites constructeurs. Insectes Soc. 6(1), 41–80 (1959)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Herianto Kurabayashi, D.: Realization of an artificial pheromone system in random data carriers using rfid tags for autonomous navigation. In: IEEE International Conference on Robotics and Automation, ICRA ’09, pp. 2288–2293 (2009)Google Scholar
  35. 35.
    Herianto Sakakibara: T., Kurabayashi, D.: Artificial pheromone system using RFID for navigation of autonomous robots. J. Bionic Eng. 4(4), 245–253 (2007)Google Scholar
  36. 36.
    Huang, W.H., Fajen, B.R., Fink, J.R., Warren, W.H.: Visual navigation and obstacle avoidance using a steering potential function. Robot. Auton. Syst. 54, 288–299 (2006)CrossRefGoogle Scholar
  37. 37.
    Ioannidis, K., Sirakoulis, G.C., Andreadis, I.: Cellular automata-based architecture for cooperative miniature robots. J Cell. Automata 8(1–2), 91–111 (2013)MathSciNetGoogle Scholar
  38. 38.
    Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall Inc, Upper Saddle River (1989)zbMATHGoogle Scholar
  39. 39.
    Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153–1160 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Konstantinidis K., Andreadis I., Sirakoulis G.C.: Chapter 3—application of artificial intelligence methods to content-based image retrieval. In: P.W. Hawkes (ed.) Advances in Imaging and Electron Physics, Advances in Imaging and Electron Physics, vol. 169, pp. 99–145. Elsevier, Amsterdam (2011)Google Scholar
  41. 41.
    Konstantinidis, K., Sirakoulis, G., Andreadis, I.: Design and implementation of a fuzzy-modified ant colony hardware structure for image retrieval. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 39(5), 520–533 (2009)CrossRefGoogle Scholar
  42. 42.
    Latombe, J.C.: Robot Motion Plann. Kluwer Academic Publishers, Norwell (1991)CrossRefGoogle Scholar
  43. 43.
    Lee, T.L., Wu, C.J.: Fuzzy motion planning of mobile robots in unknown environments. J. Intell. Robotics Syst. 37(2), 177–191 (2003)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Li, X., Orchard, M.: New edge directed interpolation. In: Proceedings of the International Conference on Image Processing, vol. 2, pp. 311–314 (2000)Google Scholar
  45. 45.
    Lin, C.T., Fan, K.W., Pu, H.C., Lu, S.M., Liang, S.F.: An hvs-directed neural-network-based image resolution enhancement scheme for image resizing. IEEE Trans. Fuzzy Syst. 15(4), 605–615 (2007)CrossRefGoogle Scholar
  46. 46.
    Liu, J., Wu, J.: Multi-Agent Robotic Systems. CRC Press, Boca Raton (2001)Google Scholar
  47. 47.
    Marchese, F.: Multiple mobile robots path-planning with MCA. In: International Conference on Autonomic and Autonomous Systems, ICAS ’06, pp. 56–56 (2006)Google Scholar
  48. 48.
    Marchese, F.M.: A directional diffusion algorithm on cellular automata for robot path-planning. Future Gener. Comput. Syst. 18(7), 983–994 (2002). Selected papers from CA2000 (6th International Workshop on Cellular Automata of IFIP working group 1.5, Osaka, Japan, 21–22 Aug 2000) and ACRI2000 (4th International Conference on Cellular Automata in Research and Industry, Karlsruhe, Germany, 4–6 OctGoogle Scholar
  49. 49.
    Martens, D., De Backer, M., Haesen, R., Vanthienen, J., Snoeck, M., Baesens, B.: Classification with ant colony optimization. IEEE Trans. Evol. Comput. 11(5), 651–665 (2007)CrossRefGoogle Scholar
  50. 50.
    Mastellone, S., Stipanovic, D, Spong, M.: Remote formation control and collision avoidance for multi-agent nonholonomic systems. In: IEEE International Conference on Robotics and Automation, pp. 1062–1067 (2007)Google Scholar
  51. 51.
    Michel, O.: Webots: Professional mobile robot simulation. J. Adv. Robot. Syst. 1(1), 39–42 (2004)Google Scholar
  52. 52.
    Muresan, D., Parks, T.: Adaptively quadratic (aqua) image interpolation. IEEE Trans. Image Process. 13(5), 690–698 (2004)CrossRefGoogle Scholar
  53. 53.
    Murphy, R.: Human-robot interaction in rescue robotics. IEEE Trans. Syst. Man Cyber. Part C Appl. Rev. 34(2), 138–153 (2004)CrossRefGoogle Scholar
  54. 54.
    Omohundro, S.: Modelling cellular automata with partial differential equations. Physica D: Nonlinear Phenomena 10(1–2), 128–134 (1984)MathSciNetCrossRefGoogle Scholar
  55. 55.
    Patnaik, S., Karibasappa, K.: Motion planning of an intelligent robot using ga motivated temporal associative memory. Appl. Artif. Intell. 19(5), 515–534 (2005)CrossRefGoogle Scholar
  56. 56.
    Progias, P., Sirakoulis, G.C.: An fpga processor for modelling wildfire spreading. Math. Comput. Modell. 57(5-6), 1436–1452 (2013)Google Scholar
  57. 57.
    Recio, G., Martin, E., Estebanez, C., Saez, Y.: Antbot: Ant colonies for video games. IEEE Trans. Comput. Intell. AI Game. 4(4), 295–308 (2012)CrossRefGoogle Scholar
  58. 58.
    Russell, R.A.: Heat trails as short-lived navigational markers for mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, pp. 3534–3539 (1997)Google Scholar
  59. 59.
    Shen, H., Zhang, L., Huang, B., Li, P.: A map approach for joint motion estimation, segmentation, and super resolution. IEEE Trans. Image Process. 16(2), 479–490 (2007)MathSciNetCrossRefGoogle Scholar
  60. 60.
    Stentz, A.: 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. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1995)Google Scholar
  61. 61.
    Tan, K.C., Tan, K., Lee, T., Zhao, S., Chen, Y.J.: Autonomous robot navigation based on fuzzy sensor fusion and reinforcement learning. In: Proceedings of the IEEE International Symposium on Intelligent Control, pp. 182–187 (2002)Google Scholar
  62. 62.
    Toffoli, T.: Cellular automata as an alternative to (rather than an approximation of) differential equations in modeling physics. Physica D: Nonlinear Phenomena 10(1–2), 117–127 (1984)MathSciNetCrossRefGoogle Scholar
  63. 63.
    Tzionas, P., Thanailakis, A., Tsalides, P.: Collision-free path planning for a diamond-shaped robot using two-dimensional cellular automata. IEEE Trans. Robot. Autom. 13(2), 237–250 (1997)CrossRefGoogle Scholar
  64. 64.
    Ulam, S.: Random processes and transformations. Int. Congr. Math. 2, 264–275 (1952)MathSciNetGoogle Scholar
  65. 65.
    Von Neumann, J., Burks, A. et al.: Theory of Self-Reproducing Automata. University of Illinois Press, Urbana (1966)Google Scholar
  66. 66.
    Wang, C., Soh, Y., Wang, H., Wang, H.: A hierarchical genetic algorithm for path planning in a static environment with obstacles. In: IEEE CCECE2002 Canadian Conference on Electrical and Computer Engineering, vol. 3, pp. 1652–1657 (2002)Google Scholar
  67. 67.
    Willms, A., Yang, S.X.: An efficient dynamic system for real-time robot-path planning. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(4), 755–766 (2006)CrossRefGoogle Scholar
  68. 68.
    Willms, A.R., Yang, S.X.: An efficient dynamic system for real-time robot-path planning. IEEE Trans. Syst. Man Cybern. Part B 36(4), 755–766 (2006)Google Scholar
  69. 69.
    Yang, S.X., Luo, C.: A neural network approach to complete coverage path planning. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(1), 718–724 (2004)CrossRefGoogle Scholar
  70. 70.
    Zheng, T., Zhao, X.: Research on optimized multiple robots path planning and task allocation approach. In: IEEE International Conference on Robotics and Biomimetics, ROBIO ’06, pp. 1408–1413 (2006)Google Scholar
  71. 71.
    Zhong, Y., Shirinzadeh, B., Tian, Y.: A new neural network for robot path planning. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1361–1366 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Konstantinos Ioannidis
    • 1
    Email author
  • Georgios Ch. Sirakoulis
    • 1
  • Ioannis Andreadis
    • 1
  1. 1.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece

Personalised recommendations