Abstract
In the complex environment, using traditional pigeon-inspired optimization algorithm for the UAV route planning leads local optimum and slow convergence speed and unstable problem. In order to solve this problem, this paper introduces an adaptive weighted pigeon-inspired optimization algorithm. The adaptive weight coefficient is applied to calculate the speed and position of the individuals in the population which enhances the quality and efficiency of route planning. In addition, this paper done many simulation experiment from different aspect for providing enough evident in path planning. This paper also have these simulation experiment by designing different environment model including the simple and complex environment. The simulation results show that the adaptive weighted pigeon-inspired optimization algorithm provides a shorter route distance, a lower threat cost consumption and the algorithm running time while comparing with pigeon-inspired optimization algorithm and particle swarm optimization than basic pigeon-inspired optimization algorithm and Particle swarm intelligence algorithm. After the spline smoothing, the UAV route is flyable.
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
Similar content being viewed by others
References
Besada-Portas, E., de la Torre, L., de la Cruz, J.M., Andres-Toro, B.: Evolutionary trajectory planner for multiple UAVs in realistic scenarios. IEEE Trans. Robot. 26(4), 619–634 (2010)
Zheng, C., Li, L., Xu, F., Sun, F., Ding, M.: Evolutionary route planner for unmanned air vehicles. IEEE Trans. Robot. 21(4), 609–620 (2005)
Nikolos, I.K., Valavanis, K.P., Tsourveloudis, N.C., Kostaras, A.N.: Evolutionary algorithm based offline/online path planner for UAV navigation. IEEE Trans. Syst. Man Cybern. B Cybern. 33(6), 898–912 (2003)
Szczerba, R.J., Galkowski, P., Glicktein, I.S., Ternullo, N.: Robust algorithm for real-time route planning. IEEE Trans. Aerosp. Electron. Syst. 36(3), 869–878 (2000)
Obermeyer, K.J., Oberlin, P., Darbha, S.: Sampling-based path planning for a visual reconnaissance unmanned air vehicle. J. Guidance, Control, Dynamics 35(2), 619–631 (2012)
Nikolos, I.K., Tsourveloudis, N.C., Valavanis, K.P.: Evolutionary algorithm based path planning for multiple UAV cooperation. In: Valavanis, K.P. (ed.) Advances in Unmanned Aerial Vehicles, vol. 33, pp. 309–340. Springer, Dordrecht (2007)
De La Cruz, J.M., Besada-Portas, E., de la Torre, L., Andres-Toro, B., Lopez-Orozco, J.A.: Evolutionary path planner for UAVs realistic environments. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1477–1484 (2008)
Sujit, P.B., Beard, R.: Multiple UAV path planning using anytime algorithms. In: Proceeding of the American Control Conference, pp. 2978–2983 (2009)
Goel, S.: Pigeon optimization algorithm: a novel approach for solving optimization problems. In: International Conference on Data Mining and Intelligent Computing (2014)
Duan, H., Qiao, P.: Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int. J. Intell. Comput. Cybern. 7(1), 24–37 (2015)
Zhang, B., Duan, H.: Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment. IEEE/ACM Trans. Comput. Biol. Bioinform. 14, 97–107 (2016)
Zhang, S., Duan, H.: Gaussian pigeon-inspired optimization approach to orbital spacecraft formation reconfiguration. Chin. J. Aeronaut. 28(1), 200–205 (2015)
Nagahara, M., Martin, C.F.: L1 Control theoretic smoothing splines. IEEE Signal Process. Lett. 21(11), 1394–1397 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Lin, N., Huang, S., Gong, C., Zhao, L., Tang, J. (2017). UAV Path Planning Based on Adaptive Weighted. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-70136-3_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70135-6
Online ISBN: 978-3-319-70136-3
eBook Packages: Computer ScienceComputer Science (R0)