Skip to main content
Log in

Minimum-Time Trajectory Planning for a Differential Drive Mobile Robot Considering Non-slipping Constraints

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

We propose a real-time minimum-time trajectory planning strategy with obstacle avoidance for a differential-drive mobile robot in the context of robot soccer. The method considers constraints important to maximize the system’s performance, such as the actuator limits and non-slipping conditions. We also present a novel friction model that regards the imbalance of normal forces on the wheels due to the acceleration of the robot. Theoretical guarantees on how to obtain a minimum-time velocity profile on a predetermined parametrized curve considering the modeled constraints are also presented. Then, we introduce a nonlinear, non-convex, local optimization using a version of the Resilient Propagation algorithm that minimizes the time of the curve while avoiding obstacles and respecting system constraints. Finally, employing a new proposed benchmark, we verified that the presented strategy allows the robot to traverse a cluttered field (with dimensions of 1.5 m \(\times \) 1.3 m) in 2.8 s in 95% of the cases, while the optimization success rate was 85%. We also demonstrated the possibility of running the optimization in real-time, since it takes less than 13.8 ms in 95% of the cases.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Ben-Asher, J. Z., Wetzler, M,, Rimon, E. D., & Diepolder, J. (2019). Optimal trajectories for a mobile robot with bounded accelerations in the presence of a wall or a bounded obstacle. In 2019 27th mediterranean conference on control and automation (MED) (pp. 481–488). IEEE.

  • Byrd, R. H., Curtis, F. E., & Nocedal, J. (2010). Infeasibility detection and SQP methods for nonlinear optimization. SIAM Journal on Optimization, 20(5), 2281–2299.

    Article  MathSciNet  MATH  Google Scholar 

  • 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.

    Article  MathSciNet  MATH  Google Scholar 

  • Haddad, M., Chettibi, T., Hanchi, S., & Lehtihet, H. (2007). A random-profile approach for trajectory planning of wheeled mobile robots. European Journal of Mechanics-A/Solids, 26(3), 519–540.

    Article  MathSciNet  MATH  Google Scholar 

  • Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100–107.

    Article  Google Scholar 

  • Hérissé, B., & Pepy, R. (2013). Shortest paths for the Dubins’ vehicle in heterogeneous environments. In 52nd IEEE conference on decision and control (pp. 4504–4509). IEEE.

  • Ho, Y. J., & Liu, J. S. (2009). Collision-free curvature-bounded smooth path planning using composite bezier curve based on voronoi diagram. In 2009 IEEE international symposium on computational intelligence in robotics and automation (CIRA), Daejeon, South Korea (pp. 463–468). IEEE.

  • IEEE. (2008). Rules for the IEEE very small competition. http://www.cbrobotica.org/wp-content/uploads/2014/03/VerySmall2008_en.pdf. Accessed April 20th, 2019.

  • Karaman, S., & Frazzoli, E. (2010). Incremental sampling-based algorithms for optimal motion planning. Robotics Science and Systems VI, 104(2).

  • Khatib, O., & Le Maitre, J. (1978). Dynamic control of manipulators operating in a complex environment. In 3rd CISM-IFToMM symposium on theory and practice of robots and manipulators, PWN, Udine, Italy, vol. 267.

  • Kim, Y. J., Kim, J. H., & Kwon, D. S. (2001). Evolutionary programming-based univector field navigation method for past mobile robots. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 31(3), 450–458.

    Article  Google Scholar 

  • Latombe, J. C. (2012). Robot motion planning (Vol. 124). Boston: Springer.

    Google Scholar 

  • LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical report, Ames, IA, USA.

  • LaValle, S. M. (2006). Planning algorithms. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  • LaValle, S. M., & Kuffner, J. J, Jr. (2001). Randomized kinodynamic planning. The International Journal of Robotics Research, 20(5), 378–400.

    Article  Google Scholar 

  • Lepetič, M., Klančar, G., Škrjanc, I., Matko, D., & Potočnik, B. (2003). Time optimal path planning considering acceleration limits. Robotics and Autonomous Systems, 45(3–4), 199–210.

    Article  Google Scholar 

  • Lim, Y., Choi, S. H., Kim, J. H., & Kim, D. H. (2008). Evolutionary univector field-based navigation with collision avoidance for mobile robot. IFAC Proceedings Volumes, 41(2), 12787–12792.

    Article  Google Scholar 

  • Lozano-Pérez, T., & Wesley, M. A. (1979). An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM, 22(10), 560–570.

    Article  Google Scholar 

  • Manor, G., Ben-Asher, J. Z., & Rimon, E. (2018). Time optimal trajectories for a mobile robot under explicit acceleration constraints. IEEE Transactions on Aerospace and Electronic Systems, 54(5), 2220–2232.

    Article  Google Scholar 

  • Metropolis, N., & Ulam, S. (1949). The monte carlo method. Journal of the American Statistical Association, 44(247), 335–341.

    Article  MathSciNet  MATH  Google Scholar 

  • Morsali, M., Frisk, E., & Åslund, J. (2019). Deterministic trajectory planning for non-holonomic vehicles including road conditions. Safety and Comfort Factors, 52(5), 97–102.

    Google Scholar 

  • Okuyama, I. F. (2019). Minimum-time obstacle avoidant trajectory planning for a differential drive robot considering motor and no-slipping constraints. Master’s thesis, Instituto Tecnológico de Aeronáutica.

  • Okuyama, I. F., Maximo, M. R. O. A., Cavalcanti, A. L. O., & Afonso, R. J. M. (2017). Nonlinear grey-box identification of a differential drive mobile robot. In XIII Simpósio Brasileiro de Automação Inteligente., SBAI, Porto Alegre, RS, BR.

  • Purwin, O., & D’Andrea, R. (2006). Trajectory generation and control for four wheeled omnidirectional vehicles. Robotics and Autonomous Systems, 54(1), 13–22.

    Article  Google Scholar 

  • Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In IEEE international conference on neural networks, vol. 1 (pp. 586–591). IEEE, San Francisco, CA, USA.

  • Rimon, E., Koditschek, D. E. (1992). Exact robot navigation using artificial potential functions. Departmental Papers (ESE) p. 323.

  • Russell, S., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall Press.

    MATH  Google Scholar 

  • Sherback, M., Purwin, O., & D’Andrea, R. (2006). Real-time motion planning and control in the 2005 Cornell RoboCup system. Robot Motion and Control (pp. 245–263). London: Springer.

    Chapter  MATH  Google Scholar 

  • Shin, K., & McKay, N. (1985). Minimum-time control of robotic manipulators with geometric path constraints. IEEE Transactions on Automatic Control, 30(6), 531–541.

    Article  MATH  Google Scholar 

  • Sprunk, C. (2008). Planning motion trajectories for mobile robots using splines. Technical Report, Freiburg, Germany.

  • Webb, D. J., & van den Berg, J. (2013). Kinodynamic RRT*: Asymptotically optimal motion planning for robots with linear dynamics. In 2013 IEEE international conference on robotics and automation (pp. 5054–5061). Karlsruhe, Germany. IEEE.

  • Yamamoto, M., Iwamura, M., & Mohri, A. (1998). Time-optimal motion planning of skid-steer mobile robots in the presence of obstacles. In Proceedings of 1998 IEEE/RSJ international conference on intelligent robots and systems. innovations in theory, practice and applications vol. 1 (pp. 32–37). IEEE, Victoria, BC, Canada.

  • Yamamoto, M., Iwamura, M., & Mohri, A. (1999). Quasi-time-optimal motion planning of mobile platforms in the presence of obstacles. In Proceedings 1999 ieee international conference on robotics and automation vol. 4 (pp. 2958–2963). IEEE, Detroit, MI, USA.

  • Zhu, Z., Schmerling, E., & Pavone, M. (2015). A convex optimization approach to smooth trajectories for motion planning with car-like robots. In 2015 54th IEEE conference on decision and control (CDC) (pp. 835–842). IEEE.

Download references

Acknowledgements

Igor Okuyama acknowledges the support of CAPES (fellowship Proc. #88882.161990/2017-01). Rubens Afonso acknowledges the support of CAPES (fellowship Proc. #88881.145490/2017-01) and the Federal Ministry for Education and Research of Germany through the Alexander von Humboldt Foundation. Igor Okuyama and Marcos Maximo would like to thank the ITAndroids’ sponsors: Altium, Cenic, Intel, ITAEx, Mathworks, Metinjo, Micropress, Polimold, Rapid, Solidworks, ST Microelectronics, WildLife, and Virtual Pyxis.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to I. F. Okuyama.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A Model Parameters

A Model Parameters

Table 4 presents the modeling parameters used in this work.

Table 4 Modeling parameters

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Okuyama, I.F., Maximo, M.R.O.A. & Afonso, R.J.M. Minimum-Time Trajectory Planning for a Differential Drive Mobile Robot Considering Non-slipping Constraints. J Control Autom Electr Syst 32, 120–131 (2021). https://doi.org/10.1007/s40313-020-00657-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-020-00657-x

Keywords

Navigation