Abstract
For a robot navigation system used in an unpredictable environment, it is generally effective to create a pathway that robots can track for carrying out a given task, such as reaching a goal. In the biological world, ants construct a foraging path using a volatile substance called a pheromone, which has been widely studied and whose characteristics have been used in a transportation network model. When a navigation path is constructed by autonomous agents using this pheromone model, the created potential field can be very noisy, with many local peaks due to the unsynchronized updates of the field. In this paper, a new hill-climbing algorithm is proposed. The algorithm minimizes information entropy and can track dynamic and noisy potential fields. The proposed algorithm is evaluated through a computer simulation.
References
[1] P. Ögren, E. Fiorelli and N. E. Leonard, Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment, IEEE Transactions on Automatic control 49 (8), 1292–1302 (2004)10.1109/TAC.2004.832203Search in Google Scholar
[2] S. S. Ge and Y. J. Cui, Dynamic motion planning for mobile robots using potential field method, Autonomous Robots 13, 207–222 (2002)10.1023/A:1020564024509Search in Google Scholar
[3] J. Choi, J. Lee and S. Oh, Biologically inspired navigation strategies for swarm intelligence using spatial Gaussian processes, Proceedings of the 17th World Congress, International Federation of Automatic Control 593–598 (2008)10.3182/20080706-5-KR-1001.00100Search in Google Scholar
[4] D. V. Nicolau Jr., K. Burrage, D. V. Nicolau and P. K. Maini, ‘Extremotaxis’: Computing with a bacterial-inspired algorithm, BioSystems 94, 47–54 (2008)Search in Google Scholar
[5] M. M. Zavlanos and G. J. Pappas, Potential fields for maintaining connectivity of mobile networks, IEEE Transactions on Robotics 23 (4), 812–816 (2007)10.1109/TRO.2007.900642Search in Google Scholar
[6] Herianto, T. Sakakibara, T. Koiwa and D. Kurabayashi, Realization of pheromone potential field for autonomous navigation by radio frequency identification, Advanced Robotics 22, 1461–1478 (2008)Search in Google Scholar
[7] M. Vergassola, E. Villermaux and B. I. Shraiman, ‘Infotaxis’ as a strategy for searching without gradients, Nature 445 (25), 406–409 (2007)10.1038/nature05464Search in Google Scholar PubMed
[8] C. E. Shannon, A mathematical theory of communication, Bell System Technical Journal 27, 379–423 (1948)10.1002/j.1538-7305.1948.tb01338.xSearch in Google Scholar
[9] M. Dorigo, M. Birattari and T. Stützle, Ant colony optimization, IEEE Computational Intelligence Magazine, 28–39 (2006)10.1109/CI-M.2006.248054Search in Google Scholar
[10] K. Sugawara, T. Kazama and T. Watanabe, Foraging behavior of interacting robots with virtual pheromone, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 3074–3079 (2004)Search in Google Scholar
[11] B. W. Silverman, Density estimation for statistics and data analysis (Chapman & Hall/CRC, 1985)Search in Google Scholar
[12] T. Lochmatterm and A. Martinoli, Theoretical analysis of three bio-inspired plume tracking algorithms, Proceedings of the 2009 IEEE international conference on Robotics and Automation, 3195–3202 (2009)10.1109/ROBOT.2009.5152686Search in Google Scholar
[13] J. Zeil, M. Hoffmann, and J. S. Chahl, Catchment areas of panoramic home images in outdoor scenes, Journal of the Optical society of America A 20, 450–469 (2003)Search in Google Scholar
© Piljae Kim et al.
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.