Abstract
This paper proposes a hybrid neural network method to solve the UAV attack route planning problem considering multiple factors. In this method, the planning procedure is decomposed by two planners: penetration planner and attack planner. The attack planner determines a candidate solution set, which adopts Guassian Radial Basis Function Neural Networks (RBFNN) to give a quick performance evaluation to find the optimal candidate solutions. The penetration planner adopts an alterative Hopfield Neural Network (NN) to refine the candidates in a fast speed. The combined effort of the two neural networks efficiently relaxes the coupling in the planning procedure and is able to generate a near-optimal solution within low computation time. The algorithms are simple and can easily be accelerated by parallelization techniques. Detailed experiments and results are reported and analyzed.
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References
Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, Boston (1991)
Asano, T., Asano, T., Guibas, L., Hershberger, J., Imai, H.: Visibility-polygon search and Euclidean shortest path. In: 26th Symp. Found. Comp. Science, pp. 155–164 (1985)
Bortoff, S.A.: Path Planning for UAVs. In: Proceedings of the 2000 American Control Conference, vol. 1, pp. 364–368 (2000)
Robot, J.S., Peggy, G., Ira, S.G.: Robust algorithm for real-time route planning. IEEE Transactions on Aerospace and Electronic Systems 36, 869–878 (2000)
Overmars, M.H., Svestka, P.: A probabilistic learning approach to motion planning. J. In: Proc. Workshop Algorithmic Foundations Robotics, pp. 19–37 (1994)
Solano, J., Jones, D.I.: Generation of collision-free paths, a genetic approach. IEEE Colloquium on Gen. Alg. for Control Sys. Eng., 5/1–5/6 (1993)
Kozakiewicz, C., Ejiri, M.: Neural network approach to path planning for two dimensional robot motion. Proc. IEEE/RSJ (IROS 1991) 2, 818–823 (1991)
Khatib, O.: Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. Int. J. of Robotics Research 5(1), 90–99 (1986)
Leavitt, C.A.: Real-Time In-Flight Planning. Proceedings of the IEEE 1996 National Conference on Aerospace and Electronics 1, 83–89 (1996)
Fan, C., Lu, Y., Liu, H., Huang, S.: Path Planning for Mobile Robot Based on Neural Networks (in Chinese). Computer Engineering and Applications 8, 86–89 (2004)
Rana, A.S., Zalzala, A.M.S.: A Neural Networks Based Collision Detection Engine for Multi-Arm Robotic Systems. In: 5th International conference on artificial neural networks, pp. 140–145 (1997)
Ellips, M., Davoud, S.: Classic and Heuristic Approaches in Robot Motion Planning–A Chronological Review. Proceedings of world academy of science, engineering and technology 23, 101–106 (2007)
Zou, A., Hou, Z., Fu, S., Tan, M.: Neural Networks for Mobile Robot Navigation: A Survey. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 1218–1226. Springer, Heidelberg (2006)
Pashkevich, A., Kazheunikau, M.: Neural network approach to trajectory synthesis for robotic manipulators. Journal of Intelligent Manufacturing 16, 173–187 (2005)
Ranka, K., Zoran, V.: Methodology of Concept Control Synthesis to Avoid Unmoving and Moving Obstacles. Journal of Intelligent and Robotic Systems 45, 267–294 (2006)
Guang, Y., Vikram, K.: Optimal path planning for unmanned air vehicles with kinematic and tactical constraints. In: Proceedings of the 41th IEEE Conference on Decision and Control, vol. 2, pp. 1301–1306 (2002)
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Wang, N., Gu, X., Chen, J., Shen, L., Ren, M. (2009). A Hybrid Neural Network Method for UAV Attack Route Integrated Planning. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_25
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DOI: https://doi.org/10.1007/978-3-642-01513-7_25
Publisher Name: Springer, Berlin, Heidelberg
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