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RRT*-Based Algorithm for Trajectory Planning Considering Probabilistic Weather Forecasts

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Air Traffic Management and Systems IV (EIWAC 2019)

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Abstract

Convective weather and its inherent uncertainty constitute one of the major challenges in the air traffic management (ATM) system, entailing both safety hazards and economic losses. In the present work, we propose a stochastic algorithm for trajectory planning that ensures feasibility and safety of the path between two points while avoiding unsafe stormy regions. The uncertain zone to be flown is described by an ensemble of equally likely forecasts. We design a scenario-based optimal rapidly exploring random tree (SB-RRT*), and we able to dynamically allocate risk during its expansion so that a safety margin is not violated. The solution is a safe continuous trajectory that minimizes the distance covered. We present preliminary results assuming weather to be the only source of uncertainty. We consider an aircraft point-mass model at constant altitude and airspeed with manoeuvres being limited by a minimum turning radius.

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References

  1. Eurocontrol, in Performance Review Report. An Assessment of Air Traffic Management in Europe During the Calendar Year 2017. Technical Report (2017)

    Google Scholar 

  2. H. Erzberger, T. Nikoleris, R.A. Paielli, Y.C. Chu, Algorithms for control of arrival and departure traffic in terminal airspace. Proc. Inst. Mech. Eng. Part G: J. Aerospace Eng. 230(9), 1762–1779 (2016)

    Article  Google Scholar 

  3. D. González-Arribas, M. Soler, M. Sanjurjo-Rivo, M-Kamgarpour, J. Simarro, Robust aircraft trajectory planning under uncertain convective environments with optimal control and rapidly developing thunderstorms. Aerospace Sci. Technol. 89, 445–459 (2019)

    Google Scholar 

  4. S. Summers, M. Kamgarpour, J. Lygeros, C. Tomlin, A stochastic reach-avoid problem with random obstacles, in Proceedings of the 14th International Conference on Hybrid Systems: Computation and Control (HSCC’11) (ACM, New York, NY, USA, 2011), pp. 251–260

    Google Scholar 

  5. D. Hentzen, M. Kamgarpour, M. Soler, D. González-Arribas, On maximizing safety in stochastic aircraft trajectory planning with uncertain thunderstorm development. Aersopace Sci. Technol. 79, 543–553 (2018)

    Article  Google Scholar 

  6. S.M. LaValle, Rapidly-Exploring Random Trees: A New Tool for Path Planning (Iowa State University, Tech. Rep., 1998)

    Google Scholar 

  7. S.M. LaValle, J.J. Kuffner, Randomized kinodynamic planning, in Proceedings 1999 IEEE International Conference on Robotics and Automation, vol 1 (1999), pp. 473–479

    Google Scholar 

  8. S.R. Martin, S.E. Wright, J.W. Sheppard, Offline and online evolutionary bi-directional RRT algorithms for efficient re-planning in dynamic environments, in IEEE International Conference on Automation Science and Engineering (2007), pp. 1131–1136

    Google Scholar 

  9. P. Cheng, Z. Shen, S.M. LaValle, RRT-based trajectory design for autonomous automobiles and spacecraft. Arch. Control Sci. 11(4), 167–194 (2001)

    MathSciNet  MATH  Google Scholar 

  10. Y. Kuwata, J. Teo, G. Fiore, S. Karaman, E. Frazzoli, J.P. How, Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Control Syst. Technol. 17(5), 1105–1118 (2009)

    Article  Google Scholar 

  11. C.E. Tuncali, G. Fainekos, Rapidly-Exploring Random Trees-Based Test Generation for Autonomous Vehicles. Arizona State University, Technical Report (2019)

    Google Scholar 

  12. J. Kim, J.P. Ostrowski, Motion planning a aerial robot using rapidly-exploring random trees with dynamic constraints, in IEEE International Conference on Robotics and Automation, vol 2 (2003), pp. 2200–2205

    Google Scholar 

  13. K. Yang, S. Sukkarieh, 3D smooth path planning for a UAV in cluttered natural environments, in IEEE/RSJ. International Conference on Intelligent Robots and Systems (2008), pp. 794–800 (2008)

    Google Scholar 

  14. Y. Bouzid, Y. Bestaoui, H. Siguerdidjane, Quadrotor-UAV optimal coverage path planning in cluttered environment with a limited onboard energy, in IEEE 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017)

    Google Scholar 

  15. B.D. Luders, M. Kothari, J.P. How, Chance constrained RRT for probabilistic robustness to environmental uncertainty, in AIAA Guidance, Navigation and Control Conference (2010)

    Google Scholar 

  16. B.D. Luders, J.P. How, Probabilistic feasibility for nonlinear systems with non-gaussian uncertainty using RRT, in AIAA Infotech@Aerospace Conference, St. Louis, MO (2011)

    Google Scholar 

  17. L. Blackmore, A probabilistic particle control approach to optimal, robust predictive control, in AIAA Guidance, Navigation, and Control Conference and Exhibit (2006)

    Google Scholar 

  18. Guidelines on Ensemble Prediction Systems and Forecasting. World Meteorological Organization (2012)

    Google Scholar 

  19. S.M. LaValle (ed.), Planning Algorithms (Cambridge University Press, New York, NY, USA, 2006)

    MATH  Google Scholar 

  20. S. Karaman, E. Frazzoli, Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30, 846–894 (2011)

    Article  Google Scholar 

  21. Y. Matsuno, R. Kikuchi, N. Matayoshi, Robust optimal guidance algorithm for required time of arrival operations using probabilistic weather forecasts, in AIAA SciTech Forum (2019)

    Google Scholar 

  22. V. Lefkopoulos, M. Kamgarpour, Using Uncertainty Data in Chance-Constrained Trajectory Planning (2019). [Online]. Available: http://arxiv.org/abs/1904.12825

  23. A.M. Shkel, V. Lumelsky, Classification of the Dubins set. Robot. Autonom. Syst. 34, 179–202 (2001)

    Article  Google Scholar 

  24. P. Pharpatara, B. Hérissé, Y. Bestaoui, 3-D trajectory planning of aerial vehicles using RRT*. IEEE Trans. Control Syst. Technol. 25, 1116–1123 (2017)

    Article  Google Scholar 

  25. S. Karaman, E. Frazzoli, Optimal kinodynamic motion planning using incremental sampling-based methods, in 49th IEEE Conference on Decision and Control (CDC) (2010)

    Google Scholar 

  26. F. Islam, J. Nasir, U. Malik, Y. Ayaz, O. Hasan, "RRT*-smart: Rapid convergence implementation of RRT* towards optimal solution," in. IEEE International Conference on Mechatronics and Automation 2012, 1651–1656 (2012)

    Google Scholar 

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Correspondence to E. Andrés .

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Andrés, E., Kamgarpour, M., Soler, M., Sanjurjo-Rivo, M., González-Arribas, D. (2021). RRT*-Based Algorithm for Trajectory Planning Considering Probabilistic Weather Forecasts. In: Electronic Navigation Research Institute (eds) Air Traffic Management and Systems IV. EIWAC 2019. Lecture Notes in Electrical Engineering, vol 731. Springer, Singapore. https://doi.org/10.1007/978-981-33-4669-7_14

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  • DOI: https://doi.org/10.1007/978-981-33-4669-7_14

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