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Autonomous Robots

, Volume 43, Issue 8, pp 2111–2130 | Cite as

Real-time motion planning with a fixed-wing UAV using an agile maneuver space

  • Joshua M. LevinEmail author
  • Meyer Nahon
  • Aditya A. Paranjape
Article

Abstract

Small fixed-wing unmanned aerial vehicles (UAVs) are becoming increasingly capable of flying at low altitudes and in constrained environments. This paper addresses the problem of automating the flight of a fixed-wing UAV through highly constrained environments. The main contribution of this paper is the development of a maneuver space, integrating steady and transient agile maneuvers for a class of fixed-wing aircraft. The maneuver space is integrated into the rapidly-exploring random trees (RRT) algorithm. The RRT-based motion planner, together with a flight control system, is demonstrated in simulations and flight tests to efficiently generate and execute a motion plan through highly constrained 3D environments in real-time. The flight experiments—which effectively demonstrated the usage of three highly agile maneuvers—were conducted using only on-board sensing and computing.

Keywords

Aerial robotics Real-time motion planning Agile flight Control 

Notes

Acknowledgements

This research was supported by the Natural Sciences and Engineering Research Council of Canada (Grant no. PGSD3-490220-2016) and by le Fonds de Recherche du Quebec - Nature et Technologies (Grant no. 2016-PR-191001).

Supplementary material

Supplementary material 1 (mp4 10536 KB)

References

  1. Allen, R., & Pavone, M. (2015). Toward a real-time framework for solving the kinodynamic motion planning problem. In 2015 IEEE international conference on robotics and automation (ICRA) (pp. 928–934). IEEE.  https://doi.org/10.1109/ICRA.2015.7139288.
  2. Ananthkrishnan, N., & Sinha, N. K. (2001). Level flight trim and stability analysis using extended bifurcation and continuation procedure. Journal of Guidance, Control, and Dynamics, 24(6), 1225–1228.  https://doi.org/10.2514/2.4839.CrossRefGoogle Scholar
  3. Barry, A. J. (2012). Flying between obstacles with an autonomous knife-edge maneuver. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
  4. Bulka, E., & Nahon, M. (2018). Automatic control for aerobatic maneuvering of agile fixed-wing UAVs. Journal of Intelligent & Robotic Systems,.  https://doi.org/10.1007/s10846-018-0790-z.CrossRefGoogle Scholar
  5. Chitsaz, H., & LaValle, S. M. (2007). Time-optimal paths for a Dubins Airplane. In 2007 46th IEEE conference on decision and control (pp .2379–2384). IEEE.  https://doi.org/10.1109/CDC.2007.4434966.
  6. Dubins, L. E. (1957). On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. American Journal of Mathematics, 79(3), 497–516.  https://doi.org/10.2307/2372560.MathSciNetCrossRefzbMATHGoogle Scholar
  7. Frazzoli, E., Dahleh, M. A., & Feron, E. (2002). Real-time motion planning for agile autonomous vehicles. Journal of Guidance, Control, and Dynamics, 25(1), 116–129.  https://doi.org/10.2514/2.4856.CrossRefGoogle Scholar
  8. Frazzoli, E., Dahleh, M. A., & Feron, E. (2005). Maneuver-based motion planning for nonlinear systems with symmetries. IEEE Transactions on Robotics, 21(6), 1077–1091.  https://doi.org/10.1109/TRO.2005.852260.CrossRefGoogle Scholar
  9. Gavrilets, V., Frazzoli, E., Mettler, B., Piedmonte, M., & Feron, E. (2001). Aggressive maneuvering of small autonomous helicopters: A human-centered approach. The International Journal of Robotics Research, 20(10), 795–807.  https://doi.org/10.1177/02783640122068100.CrossRefGoogle Scholar
  10. He, Z., Li, D., & Lu, Y. (2018). Disturbance compensation-Based piecewise linear control design for perching maneuvers. In IEEE transactions on aerospace and electronic systems (pp. 1–16).  https://doi.org/10.1109/TAES.2018.2849898.CrossRefGoogle Scholar
  11. Karaman, S., & Frazzoli, E. (2011). Optimal kinodynamic motion planning using incremental sampling-based methods. In 49th IEEE conference on decision and control (CDC) (pp. 7681–7687). IEEE.  https://doi.org/10.1109/CDC.2010.5717430.
  12. Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research, 30(7), 846–894.  https://doi.org/10.1177/0278364911406761.CrossRefzbMATHGoogle Scholar
  13. Karaman, S., Walter, M. R., Perez, A., Frazzoli, E., & Teller, S. (2011). Anytime motion planning Using the RRT*. In 2011 IEEE international conference on robotics and automation (ICRA). IEEE.Google Scholar
  14. Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration space. IEEE Transactions on Robotics and Automation, 12(4), 566–580.  https://doi.org/10.1109/70.508439.CrossRefGoogle Scholar
  15. Khan, W. (2016). Dynamics modeling of agile fixed-wing unmanned aerial vehicles. PhD thesis, McGill University.Google Scholar
  16. Khan, W., & Nahon, M. (2013). Toward an accurate physics-based UAV thruster model. IEEE/ASME Transactions on Mechatronics, 18(4), 1269–1279.  https://doi.org/10.1109/tmech.2013.2264105.CrossRefGoogle Scholar
  17. Khan, W., & Nahon, M. (2015). Development and validation of a propeller slipstream model for unmanned aerial vehicles. Journal of Aircraft, 52(6), 1985–1994.  https://doi.org/10.2514/1.C033118.CrossRefGoogle Scholar
  18. Khan, W., & Nahon, M. (2015). Real-time modeling of agile fixed-wing UAV aerodynamics. In 2015 International conference on unmanned aircraft systems (ICUAS) (pp. 1188–1195). IEEE.  https://doi.org/10.1109/icuas.2015.7152411.
  19. Kim, H. J., Shim, D. H., & Sastry, S. (2002). Nonlinear model predictive tracking control for rotorcraft-based unmanned aerial vehicles. In Proceedings of the American control conference (pp. 3576–3581). IEEE.  https://doi.org/10.1109/ACC.2002.1024483.
  20. Kuo, B. C., & Golnaraghi, F. (2003). Automatic control systems. New York: Wiley.Google Scholar
  21. LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. TR 98-11, Computer Science Dept, Iowa State University.Google Scholar
  22. Lee, D., & Shim, D. H. (2014). RRT-based path planning for fixed-wing UAVs with arrival time and approach direction constraints. In 2014 International conference on unmanned aircraft systems (pp. 317–328). IEEE.Google Scholar
  23. Levin, J. M., Paranjape, A., & Nahon, M. (2017). Agile fixed-wing UAV motion planning with knife-edge maneuvers. In 2017 International conference on unmanned aircraft systems (ICUAS) (pp. 114–123). IEEE.  https://doi.org/10.1109/icuas.2017.7991475.
  24. Levin, J. M., Paranjape, A. A., & Nahon, M. (2018). Motion planning for a small aerobatic fixed-wing unmanned aerial vehicle. In The international conference on intelligent robots and systems (IROS). IEEE (accepted for publication).Google Scholar
  25. Levin, J. M., Paranjape, A. A., & Nahon, M. (2018). Sideslip and slipstream in extreme maneuvering with fixed-wing unmanned aerial vehicles. Journal of Guidance, Control, and Dynamics, 41(7), 1610–1616.  https://doi.org/10.2514/1.G003086.CrossRefGoogle Scholar
  26. Lugo-Cárdenas, I., Flores, G., Salazar, S., & Lozano, R. (2014). Dubins path generation for a fixed wing UAV. In 2014 International conference on unmanned aircraft systems (ICUAS) (pp. 339–346). IEEE.  https://doi.org/10.1109/ICUAS.2014.6842272.
  27. MacAllister, B., Butzke, J., Kushleyev, A., Pandey, H., & Likhachev, M. (2013). Path planning for non-circular micro aerial vehicles in constrained environments. In 2013 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3933–3940). IEEE.  https://doi.org/10.1109/ICRA.2013.6631131.
  28. Majumdar, A., & Tedrake, R. (2017). Funnel libraries for real-time robust feedback motion planning. The International Journal of Robotics Research, 36(8), 947–982.  https://doi.org/10.1177/0278364917712421.CrossRefGoogle Scholar
  29. Owen, M., Beard, R. W., & McLain, T. W. (2014). Handbook of unmanned aerial vehicles. Dordrecht: Springer.  https://doi.org/10.1007/978-90-481-9707-1120.CrossRefGoogle Scholar
  30. Paranjape, A. A., Meier, K. C., Shi, X., Chung, S. J., & Hutchinson, S. (2015). Motion primitives and 3-D path planning for fast flight through a forest. The International Journal of Robotics Research, 34(3), 357–377.  https://doi.org/10.1177/0278364914558017.CrossRefGoogle Scholar
  31. Park, S. (2012). Autonomous aerobatics on commanded path. Aerospace Science and Technology, 22(1), 64–74.  https://doi.org/10.1016/j.ast.2011.06.007.CrossRefGoogle Scholar
  32. Patterson, M. A., & Rao, A. V. (2014). GPOPS-II: A MATLAB software for solving multiple-phase optimal control problems using HP-adaptive gaussian quadrature collocation methods and sparse nonlinear programming. ACM Transactions on Mathematical Software, 41(1), 1–37.  https://doi.org/10.1145/2558904.MathSciNetCrossRefzbMATHGoogle Scholar
  33. Schouwenaars, T., How, J., & Feron, E. (2004). Receding horizon path planning with implicit safety guarantees. In Proceedings of the 2004 American control conference (pp. 5576–5581). IEEE.  https://doi.org/10.23919/ACC.2004.1384742.
  34. Schouwenaars, T., Mettler, B., Feron, E., & How, J. (2004). Hybrid model for trajectory planning of agile autonomous vehicles. Journal of Aerospace Computing, Information, and Communication, 1, 629–651.  https://doi.org/10.2514/1.12931.CrossRefGoogle Scholar
  35. Selig, M. S. (2014). Real-time flight simulation of highly maneuverable unmanned aerial vehicles. Journal of Aircraft, 51(6), 1705–1725.  https://doi.org/10.2514/1.c032370.CrossRefGoogle Scholar
  36. Snell, S. A., Enns, D. F., & Garrard, W. L. (1992). Nonlinear inversion flight control for a supermaneuverable aircraft. Journal of Guidance, Control, and Dynamics, 15(4), 976–984.  https://doi.org/10.2514/3.20932.CrossRefGoogle Scholar
  37. Sobolic, F. M. (2009). Agile flight control techniques for a fixed-wing aircraft. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
  38. Takei, R., Tsai, R., Shen, H., & Landa, Y. (2010). A practical path-planning algorithm for a simple car: A Hamilton-Jacobi approach. In Proceedings of the 2010 American control conference (pp. 6175–6180). IEEE.  https://doi.org/10.1109/ACC.2010.5531607.
  39. Vieira, H. L., & Grassi, V. (2014). Improving RRT’s efficiency through motion primitives generation optimization. In 2014 Joint conference on robotics: SBR-LARS robotics symposium and robocontrol (pp. 37–42). IEEE.  https://doi.org/10.1109/SBR.LARS.Robocontrol.2014.20.
  40. Wickenheiser, A. M., & Garcia, E. (2006). Longitudinal dynamics of a perching aircraft. Journal of Aircraft, 43(5), 1386–1392.  https://doi.org/10.2514/1.20197.CrossRefGoogle Scholar
  41. Wickenheiser, A. M., & Garcia, E. (2008). Optimization of perching maneuvers through vehicle morphing. Journal of Guidance, Control, and Dynamics, 31(4), 815–823.  https://doi.org/10.2514/1.33819.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Joshua M. Levin
    • 1
    Email author
  • Meyer Nahon
    • 1
  • Aditya A. Paranjape
    • 2
  1. 1.Department of Mechanical EngineeringMcGill UniversityMontrealCanada
  2. 2.Tata Research, Development and Design CentreTata Consultancy Services Ltd.Hadapsar, PuneIndia

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