Journal of Intelligent & Robotic Systems

, Volume 82, Issue 2, pp 301–324 | Cite as

Optimal Multi-Criteria Waypoint Selection for Autonomous Vehicle Navigation in Structured Environment

  • José Vilca
  • Lounis Adouane
  • Youcef Mezouar


This paper deals with autonomous navigation of unmanned ground vehicles (UGV). The UGV has to reach its assigned final configuration in a structured environments (e.g. a warehouse or an urban environment), and to avoid colliding neither with the route boundaries nor any obstructing obstacles. In this paper, vehicle planning/set-points definition is addressed. A new efficient and flexible methodology for vehicle navigation throughout optimal and discrete selected waypoints is proposed. Combining multi-criteria optimization and expanding tree allows safe, smooth and fast vehicle overall navigation. This navigation through way-points permits to avoid any path/trajectory planning which could be time consuming and complex, mainly in cluttered and dynamic environment. To evaluate the flexibility and the efficiency of the proposed methodology based on expanding tree (taking into account the vehicle model and uncertainties), an important part of this paper is dedicated to give an accurate comparison with another proposed optimization based on the commonly used grid map. A set of simulations, comparison with other methods and experiments, using an urban electric vehicle, are presented and demonstrate the reliability of our proposals.


Autonomous vehicle navigation Optimal planning Waypoints generation Multi-criteria optimization Vehicle’s kinematic constraints Localization under uncertainties 


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  1. 1.
    Abbadi, A., Matousek, R., Petr Minar, P.S.: RRTs review and options. In: Proceedings of the International Conference on Energy, Environment, Economics, Devices, Systems, Communications, Computers (2011)Google Scholar
  2. 2.
    Adouane, L., Benzerrouk, A., Martinet, P.: Mobile robot navigation in cluttered environment using reactive elliptic trajectories. In: Proceedings of the 18th IFAC World Congress (2011)Google Scholar
  3. 3.
    Aicardi, M., Casalino, G., Bicchi, A., Balestrino, A.: Closed loop steering of unicycle like vehicles via lyapunov techniques. J. Math. Mech. IEEE 2(1), 27–35 (1995)Google Scholar
  4. 4.
    Bellman, R.: A markovian decision process. J. Math. Mech. 6, 679–684 (1957)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Bertsekas, D.P.: Dynamic Programming and Optimal Control, vol. I. Athena Scientific (1995)Google Scholar
  6. 6.
    Bonfè, M., Secchi, C., Scioni, E.: Online trajectory generation for mobile robots with kinodynamic constraints and embedded control systems. In: Proceedings of the 10th International IFAC Symposium on Robot Control. Croatia (2012)Google Scholar
  7. 7.
    Choset, H., Lynch, K.M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L.E., Thrun, S.: Principles of Robot Motion: Theory, Algorithms, and Implementation. MIT Press (2005)Google Scholar
  8. 8.
    Connors, J., Elkaim, G.H.: Manipulating b-spline based paths for obstacle avoidance in autonomous ground vehicles. In: Proceedings of the ION National Technical Meeting, ION NTM 2007 San Diego (2007)Google Scholar
  9. 9.
    Consolini, L., Morbidi, F., Prattichizzo, D., Tosques, M.: Leader-follower formation control of nonholonomic mobile robots with input constraints. Automatica 44(5), 1343–1349 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Gu, T., Dolan, J.M.: On-road motion planning for autonomous vehicles. In: Su, C.Y., Rakheja, S., Liu, H. (eds.) Intelligent Robotics and Applications, vol. 7508. Springer Berlin Heidelberg (2012)Google Scholar
  11. 11.
    Horst, J., Barbera, A.: Trajectory generation for an on-road autonomous vehicle. Proceedings of the SPIE, Unmanned Systems Technology VIII (2006)Google Scholar
  12. 12.
    The Institut Pascal Data Sets. (2013)
  13. 13.
    Jazar, R.N.: Vehicle Dynamics: Theory and Application, Chapter 7. Springer-Verlag (2014)Google Scholar
  14. 14.
    Kallem, V., Komoroski, A., Kumar, V.: Sequential composition for navigating a nonholonomic cart in the presence of obstacles. IEEE Trans. Robot. 27(6), 1152–1159 (2011)CrossRefGoogle Scholar
  15. 15.
    Karaman, S., Frazzoli, E.: Sampling-based Algorithms for Optimal Motion Planning. Int. J. Robot. Res. 30(7), 846–894 (2011)CrossRefzbMATHGoogle Scholar
  16. 16.
    Khalil, H.K.: Nonlinear Systems. Prentice Hall (2002). (1986)Google Scholar
  17. 17.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5, 90–99 (1986)CrossRefGoogle Scholar
  18. 18.
    Kuwata, Y., Fiore, G.A., Teo, J., Frazzoli, E., How, J.P.: Motion planning for urban driving using rrt. In: International Conference on Intelligent Robots and Systems, pp. 1681–1686 (2008)Google Scholar
  19. 19.
    Labakhua, L., Nunes, U., Rodrigues, R., Leite, F.: Smooth trajectory planning for fully automated passengers vehicles: Spline and clothoid based methods and its simulation. In: Cetto, J., Ferrier, J.L., Costa dias Pereira, J.M., Filipe, J. (eds.) Informatics in Control Automation and Robotics, Lecture Notes Electrical Engineering, vol. 15, pp. 169–182. Springer Berlin Heidelberg (2008)Google Scholar
  20. 20.
    Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, Boston (1991)CrossRefzbMATHGoogle Scholar
  21. 21.
    LaValle, S. M.: Planning Algorithms. Cambridge University Press (2006)Google Scholar
  22. 22.
    Lee, J.W., Litkouhi, B.: A unified framework of the automated lane centering/changing control for motion smoothness adaptation. In: Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 282–287 (2012)Google Scholar
  23. 23.
    Luca, A.D., Oriolo, G., Samson, C.: Feedback control of a nonholonomic car-like robot. In: Laumond, J.P. (ed.) Proceedings of the Robot Motion Planning and Control, pp. 171–253. Springer-Verlag, Berlin (1998)Google Scholar
  24. 24.
    Maalouf, E., Saad, M., Saliah, H.: A higher level path tracking controller for a four-wheel differentially steered mobile robot. Robot. Auton. Syst. 54, 23–33 (2006)CrossRefGoogle Scholar
  25. 25.
    Martins, M.M., Santos, C.P., Frizera-Neto, A., Ceres, R.: Assistive mobility devices focusing on smart walkers: Classification and review. Robot. Auton. Syst. 60(4), 548–562 (2012)CrossRefGoogle Scholar
  26. 26.
    Rucco, A., Notarstefano, G., Hauser, J.: Computing minimum lap-time trajectories for a single-track car with load transfer. In: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, pp. 6321–6326 (2012)Google Scholar
  27. 27.
    Sezen, B.: Modeling automated guided vehicle systems in material handling. Otomatiklestirilmi Rehberli Arac Sistemlerinin Transport Tekniginde Modellemesi. Dou Universitesi Dergisi 4(2), 207–216 (2011)Google Scholar
  28. 28.
    Sharma, S., Taylor, M.E.: Autonomous waypoint selection for navigation and path planning: A navigation framework for multiple planning algorithms. Tech. Rep. (2012)Google Scholar
  29. 29.
    Siciliano, B., Khatib, O. (eds.): Springer Handbook of Robotics, Part E-34. Springer (2008)Google Scholar
  30. 30.
    Stoeter, S.A., Rybski, P.E., Stubbs, K.N., McMillen, C.P., Gini, M., Hougen, D.F., Papanikolopoulos, N.: A robot team for surveillance tasks: Design and architecture. Robot. Auton. Syst. 40(2-3), 173–183 (2002)CrossRefzbMATHGoogle Scholar
  31. 31.
    Szczerba, R., Galkowski, P., Glicktein, I., Ternullo, N.: Robust algorithm for real-time route planning. IEEE Trans. Aerosp. Electron. Syst. 36(3), 869–878 (2000)CrossRefGoogle Scholar
  32. 32.
    Vaz, D.A., Inoue, R.S., Grassi Jr. V.: Kinodynamic motion planning of a skid-steering mobile robot using rrts. In: Proceedings of the 2010 Latin American Robotics Symposium and Intelligent Robotics Meeting, LARS ’10, pp. 73–78. IEEE Computer Society (2010)Google Scholar
  33. 33.
    Vilca, J., Adouane, L., Mezouar, Y., Lébraly, P.: An overall control strategy based on target reaching for the navigation of an urban electric vehicle. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’13). Tokyo (2013)Google Scholar
  34. 34.
    Ziegler, J., Werling, M., Schroeder, J.: Navigating car-like robots in unstructured environment using an obstacle sensitive cost function. In: Proceedings of the IEEE Intelligent Vehicle Sympsium (IV), pp. 787–791. Netherlands (2008)Google Scholar

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© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Institut PascalBlaise Pascal University – UMR CNRSClermont-FerrandFrance

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