Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-25T00:42:08.263Z Has data issue: false hasContentIssue false

Euler–Lagrange as Pseudo-metric of the RRT algorithm for optimal-time trajectory of flight simulation model in high-density obstacle environment

Published online by Cambridge University Press:  14 December 2015

Mohammad Altaher*
Affiliation:
CS Department, University of Mansoura, Mansoura, Egypt.
Omaima Nomir
Affiliation:
CS Department, University of Mansoura, Mansoura, Egypt, IEEE member, E-mail: o.nomir@umiami.edu
*
*Corresponding author. E-mail: mohammad_altaher@yahoo.com

Summary

This paper introduces a solution to the problem of steering an aerodynamical system, with non-holonomic constraints superimposed on dynamic equations of motion. The proposed approach is a dimensionality reduction of the Optimal Control Problem (OCP) with heavy path constraints to be solved by Rapidly-Exploring Random Tree (RRT) algorithm. In this research, we formulated and solved the OCP with Euler–Lagrange formula in order to find the optimal-time trajectory. The RRT constructs a non-collision path in static, high-dense obstacle environment (i.e. heavy path constraint). Based on a real-world aircraft model, our simulation results found the collision-free path and gave improvements of time and fuel consumption of the optimized Hamiltonian-based model over the original non-optimized model.

Type
Articles
Copyright
Copyright © Cambridge University Press 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. Glassman, E. and Tedrake, R., “LQR-Based Heuristics for Rapidly Exploring State Space,” IEEE International Conference on Robotics and Automation (ICRA), IEEE, Anchorage, Alaska, 2010. pp(1).Google Scholar
2. Karaman, S. and Frazzoli, E., “Sampling-based algorithms for optimal motion planning,” The International Journal of Robotics Research. 30 (7), 846894 (June 2011).Google Scholar
3. Webb, D. J. and van den Berg, J., “Kinodynamic RRT*: Optimal Motion Planning for Systems with Linear Differential Constraints,” CoRR, vol.abs/1205.5088, (2012).Google Scholar
4. Frazzoli, E., Dahleh, M. A. and Feron, E., “Real-time motion planning for agile autonomous vehicles,” J. Guid. Control Dyn. 25 (1), 116129 (2002).Google Scholar
5. Donald, B., Xavier, P., Canny, J. and Reif, J., “Kinodynamic motion planning,” J. ACM 40 (5), 10481066 (1993).Google Scholar
6. Nazemizadeh, M. and Damavand, I., “Trajectory planning of robots in presence of obstacles: A review study,” Universal J. Comput. Anal. 1, 2432 (2013).Google Scholar
7. Karaman, S. and Frazzoli, E., “Optimal Kinodynamic Motion Planning using Incremental Sampling-Based Methods,” IEEE Conference on Decision and Control (2010).Google Scholar
8. Jeon, J., Karaman, S. and Frazzoli, E., “Anytime Computation of Time-Optimal Off-Road Vehicle Maneuvers using the RRT*,” Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on. IEEE, 2011, p(3276–3282).CrossRefGoogle Scholar
9. Perez, A., Platt, R., Konidaris, G., Kaelbling, L. and Lozano-Perez, T., “LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristics,” In Robotics and Automation (ICRA), 2012 IEEE International Conference on (pp. 2537–2542). IEEE.CrossRefGoogle Scholar
10. Tedrake, R., “LQR-trees: Feedback motion planning on sparse randomized trees,” Robot. Sci. Syst., In Proceedings of Robotics: Science and Systems, Seattle, WA. (2009) p(17–24).Google Scholar
11. Cheng, P. and LaValle, S. M., “Reducing Metric Sensitivity in Randomized Trajectory Design,” Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, Outrigger Wailea Resort, Maui, Hawaii, USA, vol. 1 (2001) pp.(43–48).Google Scholar
12. LaValle, S. M. and Kuffner, J. J., “Randomized Kinodynamic Planning,” Proceedings IEEE International Conference on Robotics and Automation The International Journal of Robotics Research, (2001). 20 (5), 378400.Google Scholar
13. Pharpatara, P., Hérissé, B., Pepy, R., & Bestaoui, Y., “Sampling-Based Path Planning: A New Tool for Missile Guidance,” Proceedings of the IFAC Symposium on Automatic Control in Aerospace, In 19th IFAC Symposium on Automatic Control in Aerospace (2013, September) (Vol. 19, No. PART 1, pp(131–136)).Google Scholar
14. Lewis, F. and Syrmos, V., Optimal Control (John Wiley & Sons, New York, 1995).Google Scholar
15. Tewari, A., Advanced Control of Aircraft, Spacecraft and Rockets, vol. 36 (John Wiley & Sons, New York, 2011).Google Scholar
16. Longuski, J. M., Guzmn, J. J. and Prussing, J. E., Optimal Control with Aerospace Applications (Springer, Berlin, 2014).Google Scholar
17. LaValle, S. M., Planning Algorithms (University of Illinois, Champaign, IL, 1999–2004).Google Scholar
18. LaValle, S. M. and Kuffner, J. Jr, “Rapidly-exploring random trees: Progress and prospects,” Workshop on the Algorithmic Foundations of Robotics, In Algorithmic and Computational Robotics: New Directions, publisher A. K. Peters, Ltd. Natick, MA, USA. The Fourth Workshop on the Algorithmic Foundations of Robotics (2000), p(293308).Google Scholar
19. Stengel, R. F., Flight Dynamics (Princeton University Press, Princeton, NJ, 2004).Google Scholar
20. Azocar, A. F., Valasek, J. and John, V. “High fidelity simulation of a nonlinear aircraft,” Am. Inst. Aeronaut. Astronaut, 52ND AEROSPACE SCIENCES MEETING. 2014. p (5–7).Google Scholar
21. Guibout, V. M., The Hamilton-Jacobi Theory for Solving Two-Point Boundary Value Problems: Theory and Numerics with Application to Spacecraft Formation Flight, Optimal Control and the Study of Phase Space Structure Ph.D. Thesis (The University of Michigan, 2004).Google Scholar
24. Sim, Y. C., Leng, S. B. and Subramaniam, V., “A combined genetic algorithms-shooting method approach to solving optimal control problems,” Int. J. Syst. Sci. 31.1, 8389 (2000).CrossRefGoogle Scholar