Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-14T05:26:55.145Z Has data issue: false hasContentIssue false
  • English
  • Français

3D UAV trajectory planning using evolutionary algorithms: A comparison study

Published online by Cambridge University Press:  27 January 2016

M. Bagherian  and
A. Alos
M. Bagherian*
Affiliation:
Applied Math Department, Faculty of Mathematical Science, University of Guilan, Rasht, Iran
*
mbagherian@guilan.ac.ir
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper focuses on the three dimensional flight path planning for an unmanned aerial vehicle (UAV) on a low altitude terrain following\terrain avoidance mission. The UAV trajectory planning problem is to compute an optimal or near-optimal trajectory for a UAV to do its mission objectives in a surviving penetration through the hostile enemy environment, considering the shape of the earth and the kinematics constraints of the UAV. Using the three dimensional terrain information, three dimensional flight path from a starting point to an end point, minimising a cost function and regarding the kinematics constraints of the UAV is calculated. The geographic information of the earth shape and enemy locations is generated using digital terrain model (DTM) and geographic information system (GIS), and is displayed in a 3D environment. Using 3D-maps containing the geographic data accompanied by DTM, and GIS, the problem is modelled by deriving the motion equations of the UAV. Two heuristic algorithms are proposed for this problem: genetic and particle swarm algorithms. Genetic and particle swarm algorithms are general purposes algorithms, because they can solve a wide range of problems, so they have to be adjusted to solve the trajectory planning problem. To test and compare the paths obtained from these algorithms, a software program is built using GIS tools and the programming languages C# and MATLAB.

Type
Research Article
Information
The Aeronautical Journal , Volume 119 , Issue 1220 , October 2015 , pp. 1271 - 1285
Copyright
Copyright © Royal Aeronautical Society 2015

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Chen, X.-G.Gao, X.-W. and Qing, D.A genetic-algorithm-based approach to UAV path planning problem, WSEAS Int Conference on Simulation, Modeling and Optimization, 17-19 August 2005, Corfu, Greece, pp 503–507.Google Scholar
2.Bellman, R.Dynamic Programming, 1962, Princeton University Press, Princeton, NJ, USA.Google Scholar
3.Betts, J.T. and Huffman, W.P.Path constrained trajectory optimization using sparse sequential quadratic programming, J Guidance, Control and Dynamics, January-February 1993, 16, (1), pp 59–68.Google Scholar
4.Rippel, E., Aharon, B.-G. and Nahum, S.Fast graph-search algorithms for general aviation flight trajectory generation, Technion, 24 May 2004, Israel Institute of Technology, Haifa, Israel.Google Scholar
5.Betts, J.T. and Huffman, W.P.Path constrained trajectory optimization using sparse sequential quadratic programming. J Guidance, Control and Dynamics, January-February 1993, 16, (1), pp 59–68.Google Scholar
6.Enright, P.J. and Conway, B.A.Discrete approximations to optimal trajectories using direct transcription and nonlinear programming, J Guidance, Control and Dynamics, July-August 1993, 15, (4), pp 994–1002.Google Scholar
7.Hall, R. Path planning and autonomous navigation for use in computer generated forces, 2007, Scientifc Report, Swedish Defense Research Agency.Google Scholar
8.Lu, P.Inverse dynamics approach to trajectory optimization for an aerospace plane, J Guidance Control and Dynamics, July-August 1993, 16, (4), pp 726–732.Google Scholar
9.Hwang, Y. and Ahuja, N.Gross motion planning a survey, ACM Computing Surveys, September 1992, 24, (3), pp 219–291.Google Scholar
10.Latombe, J.Robot Motion Planning, 1991, Kluwer, Boston, MA, USA.Google Scholar
11.Haeberling, C.Symbolization in topographic 3D-maps: conceptual aspects for user-oriented design, 1999, Zurich Institute of Cartography, Swiss Federal Institute of Technology (ETH Zurich).Google Scholar
12.Zardashti, R. and Bagherian, M.A new model for optimal TF/TA flight path design problem, Aeronaut J, May 2009, 113, (1143), pp 301–308.Google Scholar
13.Anargyros, N.K., Ioannis, K.N., Tsourveloudis, N.C. and Valavanis, K.P.Evolutionary algorithm based on-line path planner for UAV navigation, 10th Mediterranean Conference on Control and Automation, 9-12 July, 2002, Lisbon, Portugal.Google Scholar
14.Ioannis, K.N. and Kimon, P.V.Evolutionary algorithm based offine/online path planner for UAV navigation, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, December 2003, 33, (6).Google Scholar
15.Call, B.R.Obstacle avoidance for unmanned air vehicles, December 2006, Brigham Young University, USA.Google Scholar
16.Qing, L., Wei, G., Yuping, L. and Chunlin, S.Aircraft toute optimization using genetic algorithms, IEEE, USA, 2-4 September 1997, pp 394–397.Google Scholar
17.Back, T.Evolutionary Algorithms in Theory and Practice, 1996, Oxford University, UKGoogle Scholar
18.Goldberg, D.E.Genetic Algorithms in Search, Optimization, and Machine Learning, 1998, Addison Wesley, Reading, MA, USA.Google Scholar
19.Mitchell, M.An Introduction to Genetic Algorithms, 1999, Massachusetts Institute of Technology, Cambridge, MA, USA.Google Scholar
20.Pellazar, M.B.Vehicle Route Planning With Constraints Using Genetic Algorithms, 1994, IEEE, Washington, USA.Google Scholar
21.Eberhart, R. and Kennedy, J.A New optimizer using particle swarm theory, 1995, Sixth International Symposium on Micro Machine and Human Science, IEEE, Washington, DC, USA.Google Scholar
22.Coelho, L.dos. S. and Sierakowski, C.A.A software tool for teaching of particle swarm optimization fundamentals, Science Direct, 26 March 2008, Parana, Brazil.Google Scholar
23.Lee, C.-Y. and Shen, Y.-X.Optimal planning of ground grid based on particle swam algorithm, 2009, World Academy of Science, Engineering and Technology, China.Google Scholar
24.Yuhui, S.Particle swarm optimization, February 2004, IEEE Neural Networks Society, USA.Google Scholar
25.Lazinica, A.Particle Swarm Optimization, 2009, InTech, Vienna, Austria.Google Scholar
26.Roberge, V., Tarbouchi, M. and Labonte, G.Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning, industrial informatics, IEEE Transactions, 2012 9, (1), pp 132–141.Google Scholar
27.Wang, Q., Zhang, A. and Qi, L. Three dimentional path planning for UAV based on improved PSO algorithm, 2014, IEEE 26th Chinese Control and Decision Conference, pp 3981–3985.Google Scholar