Skip to main content Accessibility help

A new model for optimal TF/TA flight path design problem

  • R. Zardashti (a1) and M. Bagherian (a2)


This paper focuses on the three dimensional flight path planning for a UAV on a low altitude terrain following/terrain avoidance mission. Using an approximate grid-based discretisation scheme, we transform the continuous optimisation problem into a search problem over a finite network, and apply a variant of the shortest-path algorithm to this problem. In other words 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. A network flow model is constructed based on the digital terrain elevation data (DTED) and a layered network is obtained. The cost function for each arc is defined as the length of the arc, then a constrained shortest path algorithm which considers the kinematics and the altitude constraints of the UAV is used to obtain the best route. Moreover the important performance parameters of the UAV are discussed. Finally a new algorithm is proposed to smooth the path in order to reduce the workload of the autopilot and control system of the UAV. The numeric results are presented to verify the capability of the procedure to generate admissible route in minimum possible time in comparison to the previous procedures. So this algorithm is potentially suited for using in online systems.



Hide All
1. Al-Hasan, S. and Vachtsevanos, G., Intelligent route planning for fast autonomous vehicles operating in a large natural terrain, Robotics and Autonomous Systems, 2002, 40, pp 124.
2. Bodenhorn, C., Galkowski, P., Stiles, P., Szczerba, R. and Glickstein, I., Personalizing onboard route re-planning for recon, attack, and special operations missions, September 1997, American Helicopter Society Conference (Avionics and Crew Systems Technical Specialists Conference).
3. Hwang, Y. and Ahuja, N., Gross motion planning — a survey, ACM Computing Surveys, September 1992, 24, (3), pp 219291.
4. Latombe, J., Robot Motion Planning, 1991, Kluwer, Boston, MA.
5. Szczerba, R., Chen, D. and Uhran, I., A framed-quadtree approach for determining Euclidean shortest paths in a 2-D environment, 1997, IEEE Transactions on Robotics and Automation, October 1997, 13, (5), pp 668681.
6. Sharma, T., Williams, P., Bill, C. and Eberhard, A., Optimal three dimensional aircraft terrain following and collision avoidance, 2007, ANZIAM J, 2005, EMAC, pp C695C711.
7. Vincent, T.L. and Grantham, W.J., Nonlinear and Optimal Control Systems, 1999, John Wiley and Sons.
8. Twigg, S., Calise, A. and Johnson, E., 3D trajectory optimization for terrain following and terrain masking, 2006, AIAA Guidance, Navigation, and Control Conference and Exhibition, Keystone, CO, 2124 August 2006.
10. Bellman, R., Dynamic Programming, 1962, Princeton University Press, Princeton, NJ.
11. Cruise missile auto routing system, 2008, Transitioning Technology to the Fleet, 2-4 June 2008, Hyatt Regency, Crystal City, VA.
12. 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 5968.
13. Hall, R., Path planning and autonomous navigation for use in computer generated forces, 2007, Scientific Report, Swedish Defense Research Agency.
14. Malaek, S.M.B. and Kosari, A.R., A novel minimum time trajectory planning in terrain following flight, 2003, IEEE Aerospace and Electrical System Conference, 8, Big Sky, MT.
15. Szczerba, R. New Cell Decomposition Techniques for Planning Optimal Paths, 1996, PhD dissertation, University of Notre Dame, Notre Dame, IN.
16. Hart, P., Nilsson, N. and Raphael, B., A Formal basis for the heuristic determination of minimum cost paths, IEEE Transactions on System, Science and Cybernetics, July 1968, 4, (2), pp 100107.
17. Rippel, E., Bar-Gill, A and Shinkin, N., Fast graph-search algorithms for general aviation flight trajectory generation, 2004, Technion — Israel Institute of Technology, Haifa, Israel, 24 May 2004.
18. Cormen, T., Leiserson, C. and Rivest, R., Introduction to Algorithms, 1990, McGraw Hill, New York.
19. Ravindra, K., Ahuja, T., Magnanti, L. and James, B.O., Network flows, Theory, Algorithms and Applications, 1993, Prentice Hall Englewood Cliffs.
20. 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 9941002.
21. Lu, P., Inverse dynamics approach to trajectory optimization for an aerospace plane, J Guidance, Control and Dynamics, July-August 1993, 16, (4), pp 726732.
22. Mitchel, E.F. The use of preprocessed cruise missile data for strategic planning, 1996, Department of Defense, United States Strategic Command.
23. Rodriques, L., Defense acquisitions — achieving B-2A bomber operational requirements, July 1999, GAO/NSIAD-99-97 Report.
24. Hargraves, C.R. and Paris, S.W., Direct trajectory optimization using nonlinear programming and collocation. J Guidance, Control and Dynamics, July-August 1987, 10, (4), pp 338342.
25. Helgason, R.V., Kennington, J.L. and Lewis, K.R., Cruise missile mission planning: a heuristic algorithm for automatic path generation, J Heuristics, September 2001, 7, (5), pp 473494.
26. Akram, I.M., Pasha, A. and Iqbal, N., Optimal path planner for autonomous vehicles, Proceedings of World Academy of Science, Engineering and Technology, January 2005, 3, pp 134137.

Related content

Powered by UNSILO

A new model for optimal TF/TA flight path design problem

  • R. Zardashti (a1) and M. Bagherian (a2)


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.