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Cellular Robotic Ants Synergy Coordination for Path Planning

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Robots and Lattice Automata

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 13))

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Abstract

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.

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References

  1. Akst, J.: Send in the bots. Scientist 27(10), 45 (2013) (Cited By since (1996))

    Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  6. Blum, C.: Ant colony optimization: Introduction and recent trends. Physics of Life Reviews 2(4), 353–373 (2005)

    Article  Google Scholar 

  7. Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406, 39–42 (2000)

    Article  Google Scholar 

  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. Brown, M., Lowe, D.: Automatic panoramic image stitching using invariant features. International Journal of Computer Vision 74(1), 59–73 (2007)

    Article  Google Scholar 

  10. Burgard, W., Moors, M., Stachniss, C., Schneider, F.: Coordinated multi-robot exploration. IEEE Trans. Robot. 21(3), 376–386 (2005)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  16. Daniel, K., Nash, A., Koenig, S., Felner, A.: Theta*: any-angle path planning on grids. J. Artif. Intell. Res. 39, 533–579 (2010)

    MathSciNet  MATH  Google Scholar 

  17. Dechter, R., Pearl, J.: Generalized best-first search strategies and the optimality of a*. J. ACM 32(3), 505–536 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  19. Deneubourg, J., Goss, S.: Collective patterns and decision-making. Ethol. Ecol. Evol. 1(4), 295–311 (1989)

    Article  Google Scholar 

  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. Di Caro, G., Dorigo, M.: Antnet: Distributed stigmergetic control for communications networks. J. Artif. Int. Res. 9(1), 317–365 (1998)

    MATH  Google Scholar 

  22. Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Italy (1992)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Fax, J., Murray, R.: Information flow and cooperative control of vehicle formations. IEEE Trans. Autom. Control 49(9), 1465–1476 (2004)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Ge, S.S., Fua, C.H.: Queues and artificial potential trenches for multirobot formations. IEEE Trans. Robot. 21(4), 646–656 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    MathSciNet  Google Scholar 

  38. Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall Inc, Upper Saddle River (1989)

    MATH  Google Scholar 

  39. Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153–1160 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  42. Latombe, J.C.: Robot Motion Plann. Kluwer Academic Publishers, Norwell (1991)

    Book  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  46. Liu, J., Wu, J.: Multi-Agent Robotic Systems. CRC Press, Boca Raton (2001)

    Google Scholar 

  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. 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 Oct

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Michel, O.: Webots: Professional mobile robot simulation. J. Adv. Robot. Syst. 1(1), 39–42 (2004)

    Google Scholar 

  52. Muresan, D., Parks, T.: Adaptively quadratic (aqua) image interpolation. IEEE Trans. Image Process. 13(5), 690–698 (2004)

    Article  Google Scholar 

  53. Murphy, R.: Human-robot interaction in rescue robotics. IEEE Trans. Syst. Man Cyber. Part C Appl. Rev. 34(2), 138–153 (2004)

    Article  Google Scholar 

  54. Omohundro, S.: Modelling cellular automata with partial differential equations. Physica D: Nonlinear Phenomena 10(1–2), 128–134 (1984)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  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. 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. 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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  64. Ulam, S.: Random processes and transformations. Int. Congr. Math. 2, 264–275 (1952)

    MathSciNet  Google Scholar 

  65. Von Neumann, J., Burks, A. et al.: Theory of Self-Reproducing Automata. University of Illinois Press, Urbana (1966)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100, Xanthi, GR, Greece

    Konstantinos Ioannidis, Georgios Ch. Sirakoulis & Ioannis Andreadis

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  1. Konstantinos Ioannidis
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  2. Georgios Ch. Sirakoulis
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Correspondence to Konstantinos Ioannidis .

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  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece

    Georgios Ch. Sirakoulis

  2. Unconventional Computing Centre, University of the West of England, Bristol, United Kingdom

    Andrew Adamatzky

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Ioannidis, K., Sirakoulis, G.C., Andreadis, I. (2015). Cellular Robotic Ants Synergy Coordination for Path Planning. In: Sirakoulis, G., Adamatzky, A. (eds) Robots and Lattice Automata. Emergence, Complexity and Computation, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-10924-4_9

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