Abstract
A local interaction based hunting approach for multi-robot system in unstructured environments is proposed in this paper. The hunting task is modeled as three modes: initial leader-fixed following&search mode, leader-changeable following&search mode and hunting mode. The conditions for modes switching are given. In order to reduce the dependence on communication, an event-trigger communication scheme based on the evader’s observation state is designed. For individual robot, it integrates local information from vision system, sonar sensors and encoders in its local coordinate frame as well as modest communication data to acquire situation-suited task mode, and then makes decisions based on behaviors with appropriate local coordination rules. The experiments with physical mobile robots verify the effectiveness of the proposed approach.
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References
Cao, Y.U., Fukunaga, A.S., Kahng, A.B.: Cooperative mobile robotics: antecedents and directions. Autonomous Robots 4, 7–27 (1997)
Tan, M., Wang, S., Cao, Z.: Multi-robot Systems. Tsinghua University Press, Beijing (2005) (in Chinese)
Vidal, R., Rashid, S., Sharp, C., Shakernia, O., Kim, J., Sastry, S.: Pursuit-evasion games with unmanned ground and aerial vehicles. In: IEEE International Conference on Robotics and Automation, pp. 2948–2955 (2001)
Vidal, R., Shakernia, O., Kim, J., Shim, H., Sastry, S.: Probabilistic Pursuit-Evasion Games: Theory, Implementation, and Experimental Evaluation. IEEE Transactions on Robotics and Automation 18(5), 662–669 (2002)
Yamaguchi, H.: A cooperative hunting behavior by mobile-robot troops. International Journal of Robotics Research 18(8), 931–940 (1999)
Yamaguchi, H.: A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations. Robotics and Autonomous Systems 43(4), 257–282 (2003)
Cao, Z., Gu, N., Tan, M., Nahavandi, S., Mao, X., Guan, Z.: Multi-robot Hunting in Dynamic Environments. International Journal of Intelligent Automation and Soft Computing 14(1), 61–72 (2008)
Cao, Z., Zhang, B., Wang, S., Tan, M.: Cooperative Hunting of Multiple Mobile Robots in an Unknown Environment. Acta Automatica Sinica 29(4), 536–543 (2003)
Weitzenfeld, A., Vallesa, A., Flores, H.: A Biologically-Inspired Wolf Pack Multiple Robot Hunting Model. In: Proc. of Latin American Robotics Symposium
Li, F., Prithviraj, D.: A Stigmergy-Based Model for Solving Cooperative Pursuit-Evasion Games in Unknown Environments. In: IEEE International Conference on Self-Adaptive and Self-Organizing Systems, pp. 467–468 (2008)
Hollinger, G., Kehagias, A., Singh, S.: Probabilistic Strategies for Pursuit in Cluttered Environments with Multiple Robots. In: IEEE International Conference on Robotics and Automation, pp. 3870–3876 (2007)
Zhuang, P., Shang, Y., Shi, H.: A New Method of Using Sensor Network for Solving Pursuit-Evasion Problem. Journal of networks 2(1), 9–16 (2007)
Oh, S., Schenato, L., Chen, P., Sastry, S.: Tracking and Coordination of Multiple Agents Using Sensor Networks: System Design, Algorithms and Experiments. Proceedings of the IEEE 95(1), 234–254 (2007)
Gerkey, B.P., Thrun, S., Gordon, G.: Visibility-based pursuit-evasion with limited field of view. International Journal of Robotics Research 25(4), 299–316 (2006)
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© 2009 Springer-Verlag Berlin Heidelberg
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Zhang, W., Wang, J., Cao, Z., Yuan, Y., Zhou, C. (2009). A Local Interaction Based Multi-robot Hunting Approach with Sensing and Modest Communication. In: Xie, M., Xiong, Y., Xiong, C., Liu, H., Hu, Z. (eds) Intelligent Robotics and Applications. ICIRA 2009. Lecture Notes in Computer Science(), vol 5928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10817-4_9
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DOI: https://doi.org/10.1007/978-3-642-10817-4_9
Publisher Name: Springer, Berlin, Heidelberg
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