Abstract
With regard to modern warfare, the environmental information is changing and it’s difficult to obtain the global environmental information in advance, so real-time flight route planning capabilities of unmanned aero vehicles (UAV) is required. Quantum Particle Swarm Optimization (QPSO) is introduced to solve this optimization problem. Meanwhile, According to the threats distribution of terrain obstacles, adversarial defense radar sites and unexpected surface-to-air missile (SAM) sites, Surface of Minimum Risk (SMR) is introduced and used to form the searching space. The objective function for the proposed QPSO is to minimizing traveling time and distance, while exceeding a minimum pre-defined turning radius, without collision with any obstacle in the flying workspace. Quadrinomial and quintic polynomials are used to approach the horizon projection of the 3-D route and this simplifies the original problem to a two dimension optimization problem, thus the complexity of the optimization problem is decreased, efficiency is improved. The simulation results show that this method can meet online path planning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Latombe, J.C.: Robot motion planning. Kluwer Academic Publishers (1991)
Jolly, K.G., Ravindran, K.P., Vijayakumar, R., Sreerama Kumar, R.: Intelligent decision making in multi-agent robot soccer system through compounded artificial neural networks. International Journal of Robotics and Autonomous Systems 55(7), 589–596 (2007)
Ghatee, M., Mohades, A.: Motion planning in order to optimize the length and clearance applying a Hopfield neural network. Expert Systems with Applications 36(3), 4688–4695 (2009)
Vadakkepat, P., Penga, X., Queka, B.K., Leea, T.H.: Evolution of fuzzy behaviours for multi-robotic system. International Journal of Robotics and Autonomous System 55(2), 146–161 (2007)
Zamirian, M., Kamyad, A.V., Farahi, M.H.: A novel algorithm for solving optimal path planning problems based on parametrization method and fuzzy aggregation. Physics Letters A 373(38), 3439–3449 (2009)
Darintsev, O.V., Migranov, A.B.: Genetic algorithms-based path planning system for the motion of a group of mobile micro-robots. Journal of Computer and Systems Sciences International 6, 493–502 (2007)
Adham, A., Phon-Amnuaisuk, S., Kuan-Ho, C.: Navigating a robotic swarm in an uncharted 2D landscape. Applied Soft Computing 10(1), 149–169 (2010)
Duan, H.B., Yu, Y.X., Zhang, X.Y., Shao, S.: Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm. Simulation Modelling Practice and Theory 18(8), 1104–1115 (2010)
Avanzini, G., Ciniglio, U., de Matteis, G.: Full-Envelope Robust Control of a Shrouded-Fan Unmanned Vehicle. Journal of Guidance, Control and Dynamics 29(2), 435–443 (2006)
Fujimori, A., Terui, F., Peter, N.: Flight Control Design of an Unmanned Space Vehicle Using Gain Scheduling. Journal of Guidance, Control, and Dynamics 28(1), 96–105 (2005)
Paul, T., et al.: Modelling of UAV formation flight using 3D potential field. Simulation Modelling Practice and Theory 16, 1453–1462 (2008)
Dong, Z.N., Zhang, R.L., Chen, Z.J., Zhou, R.: Study on UAV Path Planning Approach Based on Fuzzy Virtual Force. Chinese Journal of Aeronautics 23(3), 341–350 (2010)
Nikolos, I.K., Valavanis, K.P.: Evolutionary Algorithm Based Offline/Online Path Planner for UAV Navigation. IEEE Transactions on Systems, Man, And Cybernetics—Part B: Cybernetics 33(6), 898–912 (2003)
Jolly, K.G., Sreerama Kumar, R., Vijayakumar, R.: A Bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits. International Journal of Robotics and Autonomous Systems 57, 23–33 (2009)
Kazem, B.I., Mahdi, A.I., Oudah, A.T.: Motion Planning for a Robot Arm by Using Genetic Algorithm. Jordan Journal of Mechanical and Industrial Engineering 2(3), 131–136 (2008)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Sun, J., Feng, B., Xu, W.B.: Particle swarm optimization with particles having quantum behaviork. In: Proceedings of, Congress on Evolutionary Computation, USA, pp. 325–331 (2004)
Xi, M., Sun, J., Xu, W.: Quantum-Behaved Particle Swarm Optimization for designing H infinity structured specified controllers. In: DCABES 2006 Proceedings, vol. 2, pp. 42–46. Shanghai University Press, Shanghai (2006)
Sun, J., Xu, W., Feng, B.: Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Big Island (HI, USA), pp. 3049–3054 (2005)
Coelho, L.D.S.: A quantum particle swarm optimizer with chaotic mutation operator. Chaos, Solitons and Fractals 37, 1409–1418 (2008)
Yue, S.G., Henrich, D., Xu, X.L., Toss, S.K.: Point-to-Point Trajectory Planning of Flexible Redundant Robot Manipulators Using Genetic Algorithms. Journal of Robotica 20, 269–280 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, J., Wang, J., Cui, G. (2012). Online Path Planning for UAV Navigation Based on Quantum Particle Swarm Optimization. In: Wu, Y. (eds) Advanced Technology in Teaching - Proceedings of the 2009 3rd International Conference on Teaching and Computational Science (WTCS 2009). Advances in Intelligent and Soft Computing, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11276-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-11276-8_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11275-1
Online ISBN: 978-3-642-11276-8
eBook Packages: EngineeringEngineering (R0)