Skip to main content
SpringerLink
Log in
Book cover

Sensing and Control for Autonomous Vehicles pp 207–226Cite as

Constrained Optimal Motion Planning for Autonomous Vehicles Using PRONTO

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 474))

Abstract

This chapter provides an overview of the authors’ efforts in vehicle trajectory exploration and motion planning based on PRONTO , a numerical method for solving optimal control problems developed over the last two decades. The chapter reviews the basics of PRONTO, providing the appropriate references to get further details on the method. The applications of the method to the constrained optimal motion planning of single and multiple vehicles is presented. Interesting applications that have been tackled with this method include, e.g., computing minimum-time trajectories for a race car, exploiting the energy from the surrounding environment for long endurance missions of unmanned aerial vehicles (UAVs) , and cooperative motion planning of autonomous underwater vehicles (AUVs) for environmental surveying.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Aguiar, A.P., Rucco, A., Alessandretti, A.: A sampled-data model-predictive framework for cooperative path following of multiple robotic vehicles. In: Fossen, T.I., Pettersen, K.Y., Nijmejier, H. (eds.) Sensing and Control for Autonomous Vehicles: Applications to Land, Water and Air Vehicles. Springer, Heidelberg (2017)

    Google Scholar 

  2. Allgower, E.L., Georg, K.: Introduction to Numerical Continuation Methods. Classics in Applied Mathematics. SIAM (2003)

    Google Scholar 

  3. Bayer, F.: Time-Optimization of a Raceline: A Projection Operator Approach. Diploma Thesis, Institute for Systems Theory and Automatic Control, University of Stuttgart (2012)

    Google Scholar 

  4. Bayer, F., Hauser, J.: Trajectory optimization for vehicles in a constrained environment. In: IEEE Conference on Decision and Control, pp. 5625–5630 (2012)

    Google Scholar 

  5. Bayer, F.A., Notarstefano, G., Allgöwer, F.: A projected SQP method for nonlinear optimal control with quadratic convergence. In: IEEE Conference on Decision and Control, pp. 6463–6468 (2013)

    Google Scholar 

  6. Betts, J.: Practical Methods for Optimal Control and Estimation Using Nonlinear Programming. Society for Industrial and Applied Mathematics (2010)

    Google Scholar 

  7. Boyd, S.P., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)

    Google Scholar 

  8. Häusler, A.J.: Mission Planning for Multiple Cooperative Robotic Vehicles. Ph.D. thesis, Department of Electrical and Computer Engineering, Instituto Superior Técnico, Lisbon (2015)

    Google Scholar 

  9. Häusler, A.J., Saccon, A., Pascoal, A.M., Hauser, J., Aguiar, A.P.: Cooperative AUV motion planning using terrain information. In: MTS/IEEE OCEANS Conference (2013)

    Google Scholar 

  10. Häusler, A.J., Saccon, A., Aguiar, A.P., Hauser, J., Pascoal, A.M.: Energy-optimal motion planning for multiple robotic vehicles with collision avoidance. IEEE Trans. Conrol Sys. Technol. 24(3), 867–883 (2016)

    Article  Google Scholar 

  11. Hauser, J.: A projection operator approach to the optimization of trajectory functionals. In: 15th IFAC World Congress, pp. 377–382 (2002)

    Google Scholar 

  12. Hauser, J.: On the computation of optimal state transfers with application to the control of quantum spin systems. In: IEEE American Control Conference, pp. 2169–2174 (2003)

    Google Scholar 

  13. Hauser, J., Meyer, D.G.: The trajectory manifold of a nonlinear control system. In: IEEE Conference on Decision and Control, pp. 1034–1039 (1998)

    Google Scholar 

  14. Hauser, J., Saccon, A.: A barrier function method for the optimization of trajectory functionals with constraints. In: IEEE Conference on Decision and Control, pp. 864–869 (2006)

    Google Scholar 

  15. Hauser, J., Saccon, A., Frezza, R.: Aggressive motorcycle trajectories. In: IFAC Symposium on Nonlinear Control Systems (2004)

    Google Scholar 

  16. Hauser, J., Saccon, A., Frezza, R.: Achievable motorcycle trajectories. In: IEEE Conference on Decision and Control, pp. 3944–3949 (2004)

    Google Scholar 

  17. Hauser, J., Sastry, S., Meyer, G.: Nonlinear control design for slightly nonminimum phase systems: application to V/STOL aircraft. Automatica 28(4), 665–679 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  18. Luemberger, D.G.: Optimization by Vector Space Methods. Wiley, New York (1969)

    Google Scholar 

  19. Notarnicola, I., Bayer, F.A., Notarstefano, G., Allgwer, F.: Final-state constrained optimal control via a projection operator approach. In: European Control Conference, pp. 148–153 (2016)

    Google Scholar 

  20. Notarstefano, G., Hauser, J., Frezza, R.: Computing Feasible Trajectories for Control-Constrained Systems: The PVTOL Example IFAC Symposium on Nonlinear Control Systems, pp. 354–359 (2005)

    Google Scholar 

  21. Notarstefano, G., Hauser, J., Frezza, R.: Trajectory manifold exploration for the PVTOL aircraft. In: IEEE Conference on Decision and Control, and the European Control Conference, pp. 5848–5853 (2005)

    Google Scholar 

  22. Rucco, A., Aguiar, A.P., Hauser, J.: Trajectory optimization for constrained UAVs: a virtual target vehicle approach. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 236–245 (2015)

    Google Scholar 

  23. Rucco, A., Aguiar, A.P., Hauser, J.: A virtual target approach for trajectory optimization of a general class of constrained vehicles. In: IEEE Conference on Decision and Control (CDC), pp. 5245–5250 (2015)

    Google Scholar 

  24. Rucco, A., Hauser, J., Notarstefano, G.: Optimal control of steer-braking systems: non-existence of minimizing trajectories. Optim. Control Appl. Methods 37(5), 965–979 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  25. Rucco, A., Notarstefano, G., Hauser, J.: Computing minimum lap-time trajectories for a single-track car with load transfer. In: IEEE Conference on Decision and Control, pp. 6321–6326 (2012)

    Google Scholar 

  26. Rucco, A., Notarstefano, G., Hauser, J.: An efficient minimum-time trajectory generation strategy for two-track car vehicles. IEEE Trans. Control Sys. Technol. 23(4), 1505–1519 (2015)

    Article  Google Scholar 

  27. Saccon, A. : Maneuver regulation of nonlinear systems: the challenge of motorcycle control. Ph.D. Dissertation, Department of Information Engineering, University of Padova, Italy (2006)

    Google Scholar 

  28. Saccon, A., Aguiar, A.P., Häusler, A.J., Hauser, J., Pascoal, A.M.: Constrained Motion Planning for Multiple Vehicles on SE(3). In: IEEE Conference on Decision and Control, pp. 5637–5642 (2012)

    Google Scholar 

  29. Saccon, A., Hauser, J., Aguiar, A.P.: Optimal control on lie groups: the projection operator approach. IEEE Trans. Autom. Control 58(9), 2230–2245 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  30. Saccon, A., Hauser, J., Beghi, A.: A virtual rider for motorcycles: an approach based on optimal control and maneuver regulation. In: IEEE International Symposium on Communications, Control and Signal Processing, pp. 243–248 (2008)

    Google Scholar 

  31. Saccon, A., Hauser, J., Beghi, A.: Trajectory exploration of a rigid motorcycle model. IEEE Trans. Control Sys. Technol. 20(2), 424–437 (2012)

    Article  Google Scholar 

  32. Saccon, A., Hauser, J., Beghi, A.: A virtual rider for motorcycles: maneuver regulation of a multi-body vehicle model. IEEE Trans. Control Sys. Technol. 21(2), 332–346 (2013)

    Article  Google Scholar 

  33. Saccon, A., Hauser, J., Beghi, A.: Virtual rider design: optimal manoeuvre definition and tracking. In: Tanelli, M., Corno, M., Savaresi, S.M. (eds.) Modelling, Simulation and Control of Two-Wheeled Vehicles, pp. 83–117. Wiley, New York (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Porto, Porto, Portugal

    A. Pedro Aguiar & Alessandro Rucco

  2. University of Stuttgart, Stuttgart, Germany

    Florian A. Bayer

  3. University of Colorado, Boulder, CO, USA

    John Hauser

  4. Salt and Pepper Technology GmbH & Co. KG, Bremen, Germany

    Andreas J. Häusler

  5. University of Lecce, Lecce, Italy

    Giuseppe Notarstefano

  6. Instituto Superior Técnico, Univ. Lisbon, Lisbon, Portugal

    Antonio M. Pascoal

  7. Eindhoven University of Technology, Eindhoven, The Netherlands

    Alessandro Saccon

Authors
  1. A. Pedro Aguiar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Florian A. Bayer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Hauser
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreas J. Häusler
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Giuseppe Notarstefano
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Antonio M. Pascoal
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alessandro Rucco
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alessandro Saccon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alessandro Saccon .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Thor I. Fossen

  2. Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Kristin Y. Pettersen

  3. Eindhoven University of Technology (TuE), Eindhoven, The Netherlands

    Henk Nijmeijer

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Aguiar, A.P. et al. (2017). Constrained Optimal Motion Planning for Autonomous Vehicles Using PRONTO. In: Fossen, T., Pettersen, K., Nijmeijer, H. (eds) Sensing and Control for Autonomous Vehicles. Lecture Notes in Control and Information Sciences, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-55372-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55372-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55371-9

  • Online ISBN: 978-3-319-55372-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation