Hostname: page-component-74656f9884-npj9w Total loading time: 0 Render date: 2026-11-10T05:18:33.420Z Has data issue: false hasContentIssue false
  • English
  • Français

Constrained optimal terrain following/threat avoidance trajectory planning using network flow

Published online by Cambridge University Press:  27 January 2016

R. Zardashti ,
A. A. Nikkhah  and
M. J. Yazdanpanah
R. Zardashti*
Affiliation:
Faculty of Aerospace Engineering, K.N. Toosi University of Technology, Tehran, Iran
A. A. Nikkhah*
Affiliation:
Faculty of Aerospace Engineering, K.N. Toosi University of Technology, Tehran, Iran
M. J. Yazdanpanah*
Affiliation:
Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
*
rezazardashti@dena.kntu.ac.ir
nikkhah@kntu.ac.ir
yazdan@ut.ac.ir
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

This paper focuses on the trajectory planning for a UAV on a low altitude terrain following/threat avoidance (TF/TA) mission. Using a grid-based approximated discretisation scheme, the continuous constrained optimisation problem into a search problem is transformed over a finite network. A variant of the Minimum Cost Network Flow (MCNF) to this problem is then applied. Based on using the Digital Terrain Elevation Data (DTED) and discrete dynamic equations of motion, the four-dimensional (4D) trajectory (three spatial and one time dimensions) from a starting point to an end point is obtained by minimising a cost function subject to dynamic and mission constraints of the UAV. For each arc in the grid, a cost function is considered as the combination of the arc length, fuel consumption and flight time. The proposed algorithm which considers dynamic and altitude constraints of the UAV explicitly is then used to obtain the feasible trajectory. The resultant trajectory can increase the survivability of the UAV using the threat region avoidance and the terrain masking effect. After obtaining the feasible trajectory, an improved algorithm is proposed to smooth the trajectory. The numeric results are presented to verify the capability of the proposed approach to generate admissible trajectory in minimum possible time in comparison to the previous works.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 118 , Issue 1203 , May 2014 , pp. 523 - 539
Copyright
Copyright © Royal Aeronautical Society 2014 

References

1. Al-Hasan, S. and Vachtsevanos, G. G. Intelligent route planning for fast autonomous vehicles operating in a large natural terrain, Robotics and Autonomous Systems, 2002, 40, pp 1–24.Google Scholar
2. Bodenhorn, C., Galkowski, P., Stiles, P., Szczerba, R. and Glickstein, I. Personalizing onboard route re-planning for recon, attack, and special operations missions, American Helicopter Society Conference (Avionics and Crew Systems Technical Specialists Conference), 1997.Google Scholar
3. Hwang, Y. and Ahuja, N. Gross motion planning – a survey, ACM Computing Surveys, 1992, 24, (3), pp 219–291.Google Scholar
4. Latombe, J. Robot Motion Planning, Boston, USA, MA: Kluwer, 1991.Google Scholar
5. Szczerba, R., Chen, D., Uhran, I. and J.A., A framed-quadtree approach for determining euclidean shortest paths in a 2D environment, IEEE Transactions on Robotics and Automation, 1997, 13, (5), pp 668–681.Google Scholar
6. Sharma, T., Williams, P., Bill, C. and Eberhard, A. Optimal three dimensional aircraft terrain following and collision avoidance, ANZIAM J, 2007, pp 695–711.Google Scholar
7. Vincent, T.L. and Grantham, W.J. Nonlinear and Optimal Control Systems, John Wiley and Sons, 1999.Google Scholar
8. Twigg, C.A. and Johnson, S.E. 3D trajectory optimization for terrain following and terrain masking, AIAA, Guidance, Navigation, and Control Conference and Exhibit, Keystone, Colorado, USA, pp 21–24, 2006.Google Scholar
9. www.virtualacquisitionshowcase.com/docs/2008/scientifc1-brief.pdf, 2008.Google Scholar
10. D.B.M., and Fontes, M. Optimal trees for general nonlinear network flow problems: A dynamic programming approach, Robotica, 2005, 23, (5), pp 567–580.Google Scholar
11. Du, X., Chen, H.-H. and Gu, W.-K. Neural network and genetic algorithm based global path planning in a static environment, J Zhejiang University SCIENCE, 2004.Google Scholar
12. Betts, J and H.W.P., Path constrained trajectory optimization using sparse sequential quadratic programming, J Guidance, Control and Dynamics, 1993, 16, (1), pp 59–68.Google Scholar
13. Hall, R. Path planning and autonomous navigation for use in computer generated forces, Scientific Report, Swedish Defense Research Agency, 2007.Google Scholar
14. Malaek, S. and Kosari, A.R. A novel minimum time trajectory planning in terrain following flight, IEEE Aerospace and Electrical System Conference, 8, 2003.Google Scholar
15. Szczerba, R. New cell decomposition techniques for planning optimal paths. PhD thesis, University of Notre Dame, Notre Dame, France, 1996.Google Scholar
16. Hart, P., Nilsson, P. and Raphael, B. A formal basis for the heuristic determination of minimum cost paths, IEEE Transactions on System, Science and Cybernetics, 1968, 4, (2), pp 100–107.Google Scholar
17. Rippel, B.-G.-A., E., and Shimkin, N. Fast graph-search algorithms for general aviation flight trajectory generation, TechnionIsrael Institute of Technology, Haifa, Israel, 2004.Google Scholar
18. Cormen, N., Leiserson, T., Rivest, R. and Stein, C. Introduction to Algorithms, McGraw Hill, 2002.Google Scholar
19. El-Sheimy, C. Valeo, and Habib, A. Digital Terrain Modeling. Artec House, 2005.Google Scholar
20. Ravindra, T.-L.M., Ahuja, K. and James, B.O. Network flows, Theory, Algorithms and Applications, Prentice Hall, Englewood Cliffs, 1993.Google Scholar
21. Enright, P.J. and Conway, B.A. Discrete approximations to optimal trajectories using direct transcription and nonlinear programming, J Guidance, Control and Dynamics, 1993, 14, (4), pp 994–2002.Google Scholar
22. Lu, P. Inverse dynamics approach to trajectory optimization for an aerospace plane, J Guidance, Control and Dynamics, 1993, 16, (4), pp 726–732.Google Scholar
23. Zardashti, R. and Bagherian, M. A new model for optimal tf/ta flight path design problem, Aeronaut J, 2009, 113, (1143), pp 301–308.Google Scholar
24. Miele, A. Flight Mechanics, I, Theory of Flight Paths, Addison-Wesley, Reading MA., USA, 1962.Google Scholar
25. Marler, R. and Arora, J. Survey of multi-objective optimization methods for engineering, Struct Multidisc Optim, 2004, 26, pp 369–395.Google Scholar
26. S. P, , Simulated annealing for multiobjective optimization problems, in Proc. 10th International Conference on Multiple Criteria Decision Making, 1, 1992.Google Scholar
27. Goldberg, D. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.Google Scholar
28. Fonseca, P. and Fleming, C.M. Genetic algorithms for multiobjective optimization: formulation, discussion, and generalization, The Fifth International Conference on Genetic Algorithms, 1993.Google Scholar
29. Fujimura, K. Path planning with multiple objectives, IEEE Robot Autom Mag, 1996, 3, (1), pp 33–38.Google Scholar
30. Stewart, I.C.B.S. and White, C. Multiobjective a*, J ACM, 1991, 38, (4), pp 775–814.Google Scholar
31. Wu, D.C.P. and Merz, T. On-board multi-objective mission planning for unmanned aerial vehicles, in Proc. IEEE Aerosp Conf, Big Sky, MT, USA, 2009, 7, (14), pp 1–10.Google Scholar
32. Hargraves, C.R. and Paris, S.W. Direct trajectory optimization using nonlinear programming and collocation, J Guidance, Control and Dynamics, 1987, 10, (4), pp 338–342.Google Scholar
33. Akram, M., Pasha, A. and Iqbal, N. Optimal path planner for autonomous vehicles, Proceedings of world Academy of Science, Engineering and Technology, 2005, 3, pp 134–137.Google Scholar
34. Helgason, V.R., Kennington, L.J. Lewis, , and R.K., Cruise missile mission planning: A heuristic algorithm for automatic path generation, J Heuristics, 2001, 7, (1), pp 473–494.Google Scholar
35. Engelbrecht, A. Fundamentals of Computational Swarm Intelligence, John Wiley and Sons, 2005.Google Scholar
36. Dreo, J. and Siarry, P. An ant colony algorithm aimed at dynamic continuous optimization, Applied Mathematics and Computation, 2006, 181, (1), pp 457–467.Google Scholar