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Santos, Valeria; Otero, Fernando E.B.; Johnson, Colin G.; Osorio, Fernando; Toledo, Claudio (2016)
Languages: English
Types: Unknown
Subjects: QA76
In the path planning problem for autonomous mobile robots, robots have to plan their path from the start position to the goal. In this paper, we investigate the application of the MMAS algorithm to the exploratory path planning problem, in which the robots should explore the environment at the same time they plan the path. Max-min ant system is an ant colony optimization algorithm that exploits the best solutions found. In addition, to analyze the quality of solutions obtained, we also analyze the traveled distance spent by robots in the first iteration of the algorithm. The environment is previously unknown to the robots, although it is represented by a topological map, that does not require precise information from the environment and provides a simple way to execute the navigation of the path. Thus, the paths are represented by a sequence of actions that the robots should execute to reach the goal. The navigation of the best solution found was implemented in a realistic robotic simulator. The proposed algorithm provides a very good performance in relation to a genetic algorithm and the well-known A* algorithm that deal with this problem.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] M. Zlochin, M. Birattari, N. Meuleau, and M. Dorigo, “Model-based search for combinatorial optimization: A critical survey,” Annals of Operations Research, vol. 131, no. 1, pp. 373-395.
    • [2] T. Stu¨tzle and H. H. Hoos, “Max-min ant system,” Future Gener. Comput. Syst., vol. 16, no. 9, pp. 889-914, jun 2000.
    • [3] T. Stu¨tzle, Local search algorithms for combinatorial problems - analysis, improvements, and new applications., ser. DISKI. Infix, 1999, vol. 220.
    • [4] M. Dorigo and T. Stu¨tzle, Ant Colony Optimization. Scituate, MA, USA: Bradford Company, 2004.
    • [5] D. O. Sales, F. S. Oso´rio, and D. F. Wolf, “Topological autonomous navigation for mobile robots in indoor environments using ann and fsm,” in I CBSEC: Confereˆncia Brasileira em Sistemas Embarcados Cr´ıticos, Sa˜o Carlos, Brasil, 2011.
    • [6] V. C. Santos, D. O. Sales, C. F. M. Toledo, and F. S. Oso´rio, “A hybrid ga-ann approach for autonomous robots topological navigation,” in Proceedings of the 29th Annual ACM Symposium on Applied Computing, ser. SAC '14. New York, NY, USA: ACM, 2014, pp. 148-153.
    • [7] E. Masehian and D. Sedighizadeh, “Classic and heuristic approaches in robot motion planning a chronological review,” in Proc. World Academy of Science, Engineering and Technology, 2007, pp. 101-106.
    • [8] Q. Zhu, J. Hu, W. Cai, and L. Henschen, “A new robot navigation algorithm for dynamic unknown environments based on dynamic path re-computation and an improved scout ant algorithm,” Applied Soft Computing, vol. 11, no. 8, pp. 4667 - 4676, 2011.
    • [9] Q. Zhu, J. Hu, and L. Henschen, “A new moving target interception algorithm for mobile robots based on sub-goal forecasting and an improved scout ant algorithm,” Applied Soft Computing, vol. 13, no. 1, pp. 539 - 549, 2013.
    • [10] M. P. Garcia, O. Montiel, O. Castillo, R. Sepu´lveda, and P. Melin, “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation,” Applied Soft Computing, vol. 9, no. 3, pp. 1102 - 1110, 2009.
    • [11] M. Brand, M. Masuda, N. Wehner, and X.-H. Yu, “Ant colony optimization algorithm for robot path planning,” in Computer Design and Applications (ICCDA), 2010 International Conference on, vol. 3, June 2010, pp. V3-436-V3-440.
    • [12] K. Ioannidis, G. Sirakoulis, and I. Andreadis, “Cellular ants: A method to create collision free trajectories for a cooperative robot team,” Robotics and Autonomous Systems, vol. 59, no. 2, pp. 113 - 127, 2011.
    • [13] O. Michel, “Webots: Professional mobile robot simulation,” Journal of Advanced Robotics Systems, vol. 1, no. 1, pp. 39-42, 2004.
    • [14] Y. Yao, Q. Ni, Q. Lv, and K. Huang, “A novel heterogeneous feature ant colony optimization and its application on robot path planning,” in Evolutionary Computation (CEC), 2015 IEEE Congress on, May 2015, pp. 522-528.
    • [15] V. C. Santos, C. F. M. Toledo, and F. S. Oso´orio, “An exploratory path planning method based on genetic algorithm for autonomous mobile robots,” in Evolutionary Computation (CEC), 2015 IEEE Congress on, May 2015, pp. 62-69.
    • [16] P. Hart, N. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” Systems Science and Cybernetics, IEEE Transactions on, vol. 4, no. 2, pp. 100-107, July 1968.
    • [17] S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2nd ed. Pearson Education, 2003.
    • [18] T. H. Collett, B. A. MacDonald, and B. P. Gerkey, “Player 2.0: Toward a practical robot programming framework,” in Proc. of the Australasian Conf. on Robotics and Automation (ACRA), Sydney, Australia, dec 2005.
    • [19] V. C. Santos, C. F. M. Toledo, and F. S. Oso´rio, “A hybrid approach for path planning and execution for autonomous mobile robots,” in Proceedings of 2nd Brazilian Symposium on Robotics and 11th Latin American Robotics Symposium. ACM, 2014, pp. 124-129.
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