A Multipath Planner for UAV Based on Pareto Optimization

Article Preview

Abstract:

A multipath planner for UAV based on Pareto optimization is proposed to overcome the disadvantage of planners existed. Multipath planning is modeled as a constrained multiobject optimization problem. A Pareto solution of multipath for UAV is generated by optimizing several object functions at the same time. The simulation results demonstrated the feasibility of the approach.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2356-2359

Citation:

Online since:

June 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Changwen Zheng et al.: Evolutionary Route Planner for Unmanned Air Vehicles, IEEE transactions on Robotics[J], Vol. 21(2005), pp.609-620.

DOI: 10.1109/tro.2005.844684

Google Scholar

[2] Tian Jing: Modeling and Optimization Methods for Multi-UAV Cooperative Reconnaissance Mission Planning Problem [D], National University of Defense Technology, Changsha(2007).

Google Scholar

[3] H. Zhu, C. Zheng, X. Hu, X. Li : Adaptive PSO using random inertia weight and its application in UAV path planning, in: Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, Bellingham, WA, (2008).

DOI: 10.1117/12.806636

Google Scholar

[4] J. Horn, N. Nafpliotis: Multiobjective optimization using the niched Pareto genetic algorithm, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana(1993), Champaign: IlliGAL Report NO. 93005.

DOI: 10.1109/icec.1994.350037

Google Scholar

[5] J. Horn, N. Nafpliotis, D. E. Goldberg: A niched Pareto genetic algorithm for multiobjective optimization[A], In: Proceedings of the 1st IEEE Conference on Evolutionary Computation[C], Piscataway, NJ(1994), pp.82-87.

DOI: 10.1109/icec.1994.350037

Google Scholar

[6] Shang-Jeng Tsai, et al. : An improved multi-objective particle swarm optimizer for multi-objective problems, Expert Systems with Applications, Vol. 37(2010), pp.5872-5886.

DOI: 10.1016/j.eswa.2010.02.018

Google Scholar