Formation Vector Based Shortest Path-Planning in CGF Formation

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Abstract:

In order to improve the poor reality and bad flexibility of the mapping relationship which matched entities with aim locations in traditional approximation method, the formation vector shortest path-planning method was presented in this paper. By analyzing the lack of aim path-planning in the approximation method, shortest path-planning was discussed and was improved by introducing the formation vector and the idea of pheromone. Furthermore, the improved algorithm was applied in a CGF simulation system. The experimental results showed that the mapping relationship had better reality and rationality and the possibility of collision was significantly reduced than the traditional formation change process.

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Periodical:

Advanced Materials Research (Volumes 756-759)

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3351-3355

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] P. Tabuada, G. J. Pappas, and P. Lima, Motion Feasibility of Multi-Agent Formations, IEEE Transactions On Robotics, vol. 21, March. 2005, pp.387-391.

DOI: 10.1109/tro.2004.839224

Google Scholar

[2] L. G. Yang, G. Ji, Q. S. Guo and X. C. Wang, The Simulation of Group Change in the Aggregated Computer Generated Forces, Computer Engineering and Applications, 23: 17-34, (2001).

Google Scholar

[3] T. Balch, R. C. Arkin, Behavior-based formation control for multirobot teams, IEEE Transactions on Robotics & Automation, vol. 12, Jun. 1998, pp.926-939.

DOI: 10.1109/70.736776

Google Scholar

[4] X. L. Kong, Q, Xue, and J. H. Song, Research and Realization on Formation's Change of Tank CGF Unit, 4th Chinese Conference of Virtual Reality and Visualization, Aug. 2004, pp.61-65.

Google Scholar

[5] R. W. Floyd, Algorithm 97: Shortest Path, Communications of the ACM 18, (1968).

Google Scholar

[6] Y. Zhang, Research on the virtual scheduling optimization methods of large scale theatrical performances, unpublished.

Google Scholar

[7] H. B. Duan, D. B. Wang, J. Q. Zhu, and X. H. Huang, Development on Ant Colony Algorithm Theory and Its Application, Control and Decision, vol. 19, Dec. 2004, pp.1321-1326.

Google Scholar