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Design of Evolutionary UAV Flight Route Planner Using Potential Field Approach
|關鍵字:||unmanned aerial vehicle;無人飛行載具;route planning;optimization;obstacle avoidance;路徑規劃;最佳化;障礙物閃避||出版社:||電機工程學系所||引用:|| Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs,” New York: Springer-Verlag, 1999.  K. Sugihara and J. Smith, “Genetic algorithms for adaptive motion planning of an autonomous mobile robot,” in Proceeding of IEEE International Symposium on Computational Intelligence in Robotics and Automation, Hawaii, HI, USA, pp. 138-143, 1997.  C. Zheng, L. Li, F. Xu, F. Sun, and M. Ding, “Evolutionary route planner for unmanned air vehicles,” IEEE Transactions on Robotics, vol. 21, no. 4, pp. 609-620, 2005.  C. L. Lin, J. R. Lin, and H. Y. Jan, “Singularity analysis and path planning for a MDOF manipulator,” Journal of the Chinese Institute of Engineers, 2007, to appear.  R. C. Arkin, “Motor schema based navigation for mobile robot: an approach to programming by behavior,” in Proceeding of IEEE International Conference on Robotics and Automation, Raleigh, NC, pp. 264-271, 1987.  R. Smierzchalski, “Evolutionary trajectory planning of ships in navigation traffic areas,” Journal of Marine Science and Technology, vol. 4, pp. 1-6, 1999.  R. Smierzchalski and Z. Michalewicz, “Modeling of ship trajectory in collision situations by an evolutionary algorithm,” IEEE Transactions on Evolutionary Computation, vol. 4, pp. 227-241, Sept. 2000.  J. Savage, E. Marquez, J. Pettersson, N. Trygg, A. Petersson, and M. Wahde, “Optimization of waypoint-guided potential field navigation using evolution algorithm,” in Proceeding of IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendi, Japan, pp. 3463-3468, 2004.  M. G. Park, J. H. Jeon, and M. C. Lee, “Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing,” in Proceeding of IEEE International Symposium on Industrial Electronics, Pusan, Korea, pp. 1530-1535, 2001  S. Shimoda, Y. Kuroda, and K. Iagnemma, “Potential field navigation of high speed unmanned ground vehicle on uneven terrain,” in Proceeding of IEEE International Conference on Robotics and Automation, Barcelona, Spain, pp. 2828-2833, 2005.  P. Vadakkepat, K. C. Tan, and M. L. Wang, “Evolutionary artificial potential fields and their application in real time robot path planning,” in Proceeding of IEEE Congress on Evolutionary Computation, San Diego, California, pp. 256-263, 2000.  S. Ge and Y. Cui, “Dynamic Motion Planning for Mobile Robots Using Potential Field Method,” Journal of Autonomous Robots, Vol. 13, No. 3, pp. 207-222, 2002.  S. Caselli, M. Reggiani, and R. Sbravati, “Parallel Path Planning with Multiple Evasion Strategies,” in Proceeding of IEEE International Conference on Robotics and Automation, Washington DC, pp. 1232-1237, 2002.  H. Tanner, S. Loizou, and K. Kyriakopoulos, “Nonholonomic Navigation and Control of Cooperating Mobile Manipulators,” IEEE Transaction on Robotics and Automation, Vol. 19, No. 1, 2003.  H. Haddad, M. Khatib, S. Lacroix and R. Chatila., “Reactive Navigation in Outdoor Environments using Potential Fields,” in Proceeding of IEEE International Conference on Robotics and Automation, Lewen, Belgium, Vol. 2, pp. 1232-1237, 1998.  T. Back, “Evolutionary Algorithms in Theory and Practice,” Oxford University Press, NY, 1996.  O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” in Proceeding of IEEE International Conference on Robotics and Automation, ST. Louis, Missouri, vol. 2, pp. 500-505, 1985.  J. Barraquand, B. Langois, and J. C. Latombe, “Numerical potential field techniques for robot path planning,” IEEE Transactions on Robotics and Automation, Vol. 22, pp. 224-241, 1992.||摘要:||
The idea of evolutionary algorithms is combined with a modified artificial potential field to develop a novel flight route planner for unmanned aerial vehicle's (UAV's) autonomous navigation. In this design framework, the individual candidate routes could avoid threats very quickly by introducing a virtual potential field while taking into account a variety of flight constraints. The flight route planner has been tested under a variety of scenarios and proven to be effective in the navigation of UAVs reaching destinations.
|Appears in Collections:||電機工程學系所|
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