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Novel three-dimensional optimal path planning method for vehicles with constrained pitch and yaw

Published online by Cambridge University Press:  02 November 2016

B. Wehbe
Affiliation:
Department of Mechanical Engineering, American University of Beirut, Beirut, Lebanon. E-mails: [email protected], [email protected]
S. Bazzi
Affiliation:
Department of Mechanical Engineering, American University of Beirut, Beirut, Lebanon. E-mails: [email protected], [email protected]
E. Shammas*
Affiliation:
Department of Mechanical Engineering, American University of Beirut, Beirut, Lebanon. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

This paper presents a novel method for generating three-dimensional optimal trajectories for a vehicle or body that moves forward at a constant speed and steers in both horizontal and vertical directions. The vehicle's dynamics limit the body-frame pitch and yaw rates; additionally, the climb and decent angles of the vehicle are also bounded. Given the above constraints, the path planning problem is solved geometrically by building upon the two-dimensional Dubins curves and then Pontryagin's Maximum Principle is used to validate that the proposed solution lies within the family of candidate time-optimal trajectories. Finally, given the severe boundedness constraints on the vertical motion of the system, the robustness of the proposed path planning method is validated by naturally extending it to remain applicable to high-altitude final configurations.

Type
Articles
Copyright
Copyright © Cambridge University Press 2016 

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References

1. Alterovitz, R. et al., “Motion planning under uncertainty for image-guided medical needle steering,” Int. J. Robot. Res. 27 (11–12), 13611374 (2008).Google Scholar
2. Balluchi, A. et al., “Path Tracking Control for Dubin's Cars,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Minneapolis, MN, USA, vol. 4 (1996) pp. 3123–3128.Google Scholar
3. Boissonnat, J.-D. et al., “Shortest Paths of Bounded Curvature in the Plane,” Proceedings of 1992 IEEE International Conference on Robotics and Automation, Nice, France, vol. 3 (1992) pp. 2315–2320.Google Scholar
4. Chitsaz, H. and LaValle, S. M., “Time-Optimal Paths for a Dubins Airplane,” Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, Louisiana, USA (2007) pp. 2379–2384.Google Scholar
5. Dubins, L. E., “On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents,” Am. J. Math. 79 (3), 497516 (1957).Google Scholar
6. Duindam, V. et al., “Three-dimensional motion planning algorithms for steerable needles using inverse kinematics,” Int. J. Robot. Res. 29 (7), 789800 (2010).Google Scholar
7. Furtuna, A. A. and Balkcom, D. J., “Generalizing Dubins curves: Minimum-time sequences of body-fixed rotations and translations in the plane,” Int. J. Robot. Res. 29 (6), 703726 (2010).Google Scholar
8. Gal, O. and Doytsher, Y., “Fast and efficient visible trajectories planning for the Dubins UAV model in 3D built-up environments,” Robotica 32 (01), 143163 (2014).Google Scholar
9. Hota, S. and Ghose, D., “Optimal Path Planning for An Aerial Vehicle in 3D Space,” Proceedings of the 49th IEEE Conference on Decision and Control, Atlanta, Georgia, USA (2010) pp. 4902–4907.Google Scholar
10. Jiang, K. et al., “Time-optimal smooth-path motion planning for a mobile robot with kinematic constraints,” Robotica 15 (05), 547553 (1997).Google Scholar
11. Karaman, S. and Frazzoli, E., “Sampling-based algorithms for optimal motion planning,” Int. J. Robot. Res. 30 (7), 846894 (2011).Google Scholar
12. Li, Y. et al., “Sparse Methods for Efficient Asymptotically Optimal Kinodynamic Planning,” In: Algorithmic Foundations of Robotics XI (Akin, H. Levent, Amato, Nancy M., Isler, Volkan and Stappen, A. Frank van der, eds.) (Springer 2015) pp. 263282.Google Scholar
13. Mahmoudian, N. et al., “Dynamics and control of underwater gliders I: Steady motions,” Tech. rep., Technical Report, Virginia Polytechnic Institute and State University (2009).Google Scholar
14. Markov, A. A., “Some examples of the solution of a special kind of problem on greatest and least quantities,” Soobshch. Karkovsk. Mat. Obshch 1, 250276 (1887).Google Scholar
15. McGee, T. G. and Hedrick, J. K., “Optimal path planning with a kinematic airplane model,” J. Guid. Control Dyn. 30 (2), 629633 (2007).Google Scholar
16. Neto, A. A. and Campos, M. F. M., “A Path Planning Algorithm for UAVs with Limited Climb Angle,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Saint Louis, Missouri, USA (2009) pp. 3894–3899.Google Scholar
17. Papadopoulos, G., Asymptotically Optimal Path Planning and Surface Reconstruction for Inspection Ph.D. Thesis, Cambridge, Massachusetts, USA (Massachusetts Institute of Technology, 2014).Google Scholar
18. Papadopoulos, G. et al., “Analysis of asymptotically optimal sampling-based motion planning algorithms for Lipschitz continuous dynamical systems,” (2014) arXiv preprint arXiv:1405.2872.Google Scholar
19. Pontryagin, L. et al., The Mathematical Theory of Optimal Processes (John Wiley, Interscience, New York, 1962).Google Scholar
20. Reeds, J. and Shepp, L., “Optimal paths for a car that goes both forwards and backwards,” Pac. J. Math. 145 (2), 367393 (1990).Google Scholar
21. Sussmann, H. J., “Shortest 3-Dimensional Paths with a Prescribed Curvature Bound,” Proceedings of the 34th IEEE Conference on Decision and Control, New Orleans, Louisiana, USA, vol. 4 (1995) pp. 3306–3312.Google Scholar
22. Sussmann, H. J. and Tang, G., “Shortest paths for the Reeds-Shepp car: A worked out example of the use of geometric techniques in nonlinear optimal control,” Rutgers Cent. Syst. Control Tech. Rep. 10, 171 (1991).Google Scholar
23. Webster, R. J. et al., “Nonholonomic modeling of needle steering,” Int. J. Robot. Res. 25 (5–6), 509525 (2006).Google Scholar
24. Wehbe, B. et al., “A Novel Method to Generate Three-Dimensional Paths for Vehicles with Bounded Pitch and Yaw,” Proceedings of IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Busan, South Korea (2015) pp. 1701–1706.Google Scholar
25. Wehbe, B. et al., “Dynamic Modeling and Path Planning of a Hybrid Autonomous Underwater Vehicle,” Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), Bali, Indonesia (2014) pp. 729–734.Google Scholar
26. Xidias, E. K. and Aspragathos, N. A., “Motion planning for multiple non-holonomic robots: A geometric approach,” Robotica 26 (04), 525536 (2008).Google Scholar