A Hybrid Neural Network Method for UAV Attack Route Integrated Planning

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.

[1]  Ellips Masehian,et al.  Classic and Heuristic Approaches in Robot Motion Planning A Chronological Review , 2007 .

[2]  Anatoly Pashkevich,et al.  Neural network approach to trajectory synthesis for robotic manipulators , 2005, J. Intell. Manuf..

[3]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[4]  Masakazu Ejiri,et al.  Neural network approach to path planning for two dimensional robot motion , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[5]  Mark H. Overmars,et al.  A probabilistic learning approach to motion planning , 1995 .

[6]  J. Solano,et al.  Generation of collision-free paths, a genetic approach , 1993 .

[7]  Zoran Vukic,et al.  Methodology of Concept Control Synthesis to Avoid Unmoving and Moving Obstacles (II) , 2003, J. Intell. Robotic Syst..

[8]  Robert J. Szczerba,et al.  Robust algorithm for real-time route planning , 2000, IEEE Trans. Aerosp. Electron. Syst..

[9]  Jacek M. Zurada,et al.  Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part I , 2006, International Symposium on Neural Networks.

[10]  A.M.S. Zalzala,et al.  A neural networks based collision detection engine for multi-arm robotic systems , 1997 .

[11]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1986 .

[12]  C. A. Leavitt.  Real-time in-flight planning , 1996, Proceedings of the IEEE 1996 National Aerospace and Electronics Conference NAECON 1996.

[13]  Leonidas J. Guibas,et al.  Visibility-polygon search and euclidean shortest paths , 1985, 26th Annual Symposium on Foundations of Computer Science (sfcs 1985).

[14]  Scott A. Bortoff,et al.  Path planning for UAVs , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[15]  Vikram Kapila,et al.  Optimal path planning for unmanned air vehicles with kinematic and tactical constraints , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[16]  Min Tan,et al.  Neural Networks for Mobile Robot Navigation: A Survey , 2006, ISNN.