Skip to main content
SpringerLink
Log in

Optimal Path Computation for Autonomous Aerial Vehicles

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

In this paper, a path planning approach is developed and demonstrated for an unmanned aerial vehicle (UAV); the algorithm is applicable for autonomous robot path planning also. The main contribution of the paper is the development of an extension to the Bellman–Ford algorithm that enables incorporation of constraints directly into the algorithm during run-time. This, therefore, provides a framework for path planning, which does not cause violation of the dynamical constraints of the vehicle (or robot), such as its angle of turn. Furthermore, a procedure for computing a number of sub-optimal paths is developed so that a range of options is available for selection; the optimality of the paths is also proved. These sub-optimal paths are generated in an order of priority (optimality). An objective function is developed that models different conflicting objectives in a unified framework; these objectives can be assigned different weights. The objectives may include minimizing the length of the path, keeping the path as straight as possible, visiting areas of interest, avoiding obstacles, approaching the terminal point from a given direction, etc. The algorithm is tested for complex mission objectives, and results are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. The Global Positioning System.

References

  1. Gang L, Min-zhou D,Tao X, Liang W. Multi-agent path planning for unmanned aerial vehicle based on threats analysis. In: Proceedings of the 3rd International workshop on intelligent systems and applications (ISA), (Wuhan, China); 2011.

  2. Kan E, Lim M, Yeo S, Ho J, Shao Z. Contour based path planning with {B}-spline trajectory generation for unmanned aerial vehicles (UAVs) over hostile terrain. J Intell Learn Syst Appl. 2011;3(3):122–130.

    Article  Google Scholar 

  3. Kala R, Shukla A, Tiwari R. Dynamic environment robot path planning using hierarchical evolutionary algorithms. Cybernet Syst. 2010;41(6):435–454.

    Article  Google Scholar 

  4. Kala R, Shukla A, Tiwari R. Robotic path planning using evolutionary momentum-based exploration. J Exp Theor Artif In. 2011;23(4):469–495.

    Article  Google Scholar 

  5. Wzorek M, Doherty P. Reconfigurable path planning for an autonomous unmanned aerial vehicle. In: Proceedings of the 16th International conference on automated planning and scheduling, American Association for Artificial Intelligence; 2006. p. 438–441.

  6. Bortoff S. Path planning for UAVs. In Proceedings of the American Control Conference; 2000. p. 364–8.

  7. Filippis LD, Guglieri G, Quagliotti F. A minimum risk approach for path planning of uavs. J Intell Robot Syst-Special Issue Unmanned Aerial Vehicles. 2011; 61: 203–19.

    Google Scholar 

  8. McLain TW, Beard RW. Trajectory planning for coordinated rendezvous of unmanned air vehicles. In Proceedings of the AIAA Guidance, Navigation, and Control Conference; 2000.

  9. Schouwenaars T, DeMoor B, Feron E, How J. Mixed integer programming for multi-vehicle path planning. In Proceedings of the European Control Conference, (Portugal); 2001. p. 2603–8.

  10. Shanmugavel M, Tsourdos A, White B. Collision avoidance and path planning of multiple uavs using flyable paths in 3d. In Proceedings of the 15th International Conference on Methods and Models in Automation and Robotics; 2010.

  11. Kamal WA, Gu DW, Postlethwaite I. Real time trajectory planning for UAVs using MILP. In Proceedings of the CDC-ECC Conference, (Seville, Spain); 2005.

  12. Richards A, Bellingham J, Tillerson M, and How J. Coordination and control of multiple UAVs. In Proceedings of the AIAA Guidance, Navigation, and Control Conference; 2002.

  13. Richards A, How J. Aircraft trajectory planning with collision avoidance using mixed integer linear programming. In Proceedings of the American Control Conference; 2002.

  14. Richards A, How J, Schouwenaars T, Feron E. Plume avoidance maneuver planning using mixed integer linear programming. In Proceedings of the AIAA Guidance, Navigation, and Control Conference; 2001.

  15. Taha HA. Operations Research: An Introduction, 6 ed. Prentice-Hall Inc., New Jersey; 1997.

    Google Scholar 

  16. Helgason RV, Kennington JL, Lewis KR. Cruise missile mission planning: a heuristic algorithm for automatic path generation. J Heurist. 2001; 7:473–94.

    Article  Google Scholar 

  17. Kim Y, Gu DW, Postlethwaite I. Real-time path planning with limited information for autonomous unmanned air vehicles. Automatica. 2008; 44:696–712.

    Article  Google Scholar 

  18. Sipahioglu A, Yazici A, Parlaktuna O, Gurel U. Real-time tour construction for a mobile robot in a dynamic environment. J Robot Auton Syst. 2008; 56:289–384.

    Article  Google Scholar 

  19. Guzman JL, Berenguel M, Rodriguez F, Dormido S. An interactive tool for mobile robot motion planning. J Robot Auton Syst. 2008; 56:385–480.

    Article  Google Scholar 

  20. McFarland MB, Zachery RA, Taylor BK. Motion planning for reduced observability of autonomous aerial vehicles. In Proceedings of the 1999 IEEE International Conference on Control Applications; 1999. p. 231–5.

  21. Murphy RR, Introduction to AI robotics. Cambridge: MIT Press; 2000.

    Google Scholar 

  22. Huang H-P and Chung S-Y, Dynamic visibility graph for path planning. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, (Sendai, Japan); 2004.

  23. Ilari J and Torras C. 2D path planning: a configuration space heuristic approach. Int J Robot Res. 1990; 9:75–91.

    Google Scholar 

  24. Neus M and Maouche S, Motion planning using the modified visibility graph. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. 1999; 4: 12–5.

  25. Bhattacharya P and Gavrilova ML. Voronoi diagram in optimal path planning. The 4th International Symposium on Voronoi Diagrams in Science and Engineering; 2007. p. 38–47.

  26. Takahashi O, Schilling RJ. Motion planning in a plane using generalized Voronoi diagrams. IEEE Trans Robot Autom. 1989; 5:143–50.

    Article  Google Scholar 

  27. Cormen TH, Leiserson CE, and Rivest RL, Introduction to algorithms. Cambridge: MIT Press; 1990.

    Google Scholar 

  28. Nilsson NJ. Principles of Artificial Intelligence. Palo Alto, CA: Tioga Publisher Company 1980.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Control & Instrumentation, Islamabad, Pakistan

    R. Samar & W. A. Kamal

Authors
  1. R. Samar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W. A. Kamal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Samar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Samar, R., Kamal, W.A. Optimal Path Computation for Autonomous Aerial Vehicles. Cogn Comput 4, 515–525 (2012). https://doi.org/10.1007/s12559-011-9117-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-011-9117-0

Keywords

Search

Navigation