Skip to main content
SpringerLink
Log in

Experimental tests of autonomous ground vehicles with preview

  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

This paper describes the design and experimental tests of a path planning and reference tracking algorithm for autonomous ground vehicles. The ground vehicles under consideration are equipped with forward looking sensors that provide a preview capability over a certain horizon. A two-level control framework is proposed for real-time implementation of the model predictive control (MPC) algorithm, where the high-level performs on-line optimization to generate the best possible local reference respect to various constraints and the low-level commands the vehicle to follow realistic trajectories generated by the high-level controller. The proposed control scheme is implemented on an indoor testbed through networks with satisfactory performance.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. I. Kolmanovsky, N. H. McClamroch. Developments in nonholonomic control problems. Control Systems Magazine, IEEE, vol. 15, no. 6, pp. 20–36, 1995.

    Article  Google Scholar 

  2. P. Morin, C. Samson. Trajectory tracking for nonholonomic vehicles: Overview and case study. In Proceedings of the 4th International Workshop on Robot Motion and Control, IEEE, pp. 139–153, 2004.

  3. A. De Cabrol, T. Garcia, P. Bonnin, M. Chetto. A concept of dynamically reconfigurable real-time vision system for autonomous mobile robotics. International Journal of Automation and Computing, vol. 5, no. 2, pp. 174–184, 2008.

    Article  Google Scholar 

  4. Y. Yoon, J. Shin, H. J. Kim, Y. Park, S. Sastry. Model-predictive active steering and obstacle avoidance for autonomous ground vehicles. Control Engineering Practice, vol. 17, no. 7, pp. 741–750, 2009.

    Article  Google Scholar 

  5. A. Ollero, O. Amidi. Predictive path tracking of mobile robots. Applications to the CMU Navlab. In Proceedings of the 5th International Conference on Advanced Robotics, IEEE, Pisa, Italy, vol. 2, pp. 1081–1086, 1991.

    Google Scholar 

  6. D. Gu, H. Hu. Neural predictive control for a car-like mobile robot. Robotics and Autonomous Systems, vol. 39, no. 2, pp. 73–86, 2002.

    Article  Google Scholar 

  7. W. Xi, J. S. Baras. MPC based motion control of car-like vehicle swarms. In Proceedings of Mediterranean Conference on Control and Automation, Athens, Greece, pp. 1–6, 2007.

  8. D. Gu and H. Hu. Receding horizon tracking control of wheeled mobile robots. IEEE Transactions on Control Systems Technology, vol. 14, no. 4, pp. 743–749, 2006.

    Article  Google Scholar 

  9. Y. Zhu, U. Ozguner. Constrained model predictive control for nonholonomic vehicle regulation problem? In Proceedings of the 17th IFAC World Congress, Seoul, Korea, pp. 9552–9557, 2008.

  10. X. B. Hu, W. H. Chen. Model predictive control of nonlinear systems: Stability region and feasible initial control. International Journal of Automation and Computing, vol.4, no. 2, pp. 195–202, 2007.

    Article  Google Scholar 

  11. F. Kühne, W. F. Lages, J. M. G. da Silva Jr. Model predictive control of a mobile robot using linearization. In Proceedings of Mechatronics and Robotics, pp. 525–530, 2004.

  12. G. Klanar, I. Krjanc. Tracking-error model-based predictive control for mobile robots in real time. Robotics and Autonomous Systems, vol. 55, no. 6, pp. 460–469, 2007.

    Article  Google Scholar 

  13. K. Kanjanawanishkul, A. Zell. Path following for an omnidirectional mobile robot based on model predictive control. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Kobe, Japan, pp. 3341–3346, 2009.

  14. D. Q. Mayne, J. B. Rawlings, C. V. Rao, P. O. M. Scokaert. Constrained model predictive control: Stability and optimality. Automatica, vol. 36, no. 6, pp. 789–814, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  15. É. Gyurkovics, A. M. Elaiw. Stabilization of sampled-data nonlinear systems by receding horizon control via discretetime approximations. Automatica, vol. 40, no. 12, pp. 2017–2028, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  16. Y. Kanayama, Y. Kimura, F. Miyazaki, T. Noguchi. A stable tracking control method for a non-holonomic mobile robot. In Proceedings of IEEE/RSJ International Workshop on Intelligent, Robots and Systems, IEEE, Osaka, Japan, vol. 3, pp. 1236–1241, 1991.

    Article  Google Scholar 

  17. W. H. Chen, D. J. Ballance, J. O’Reilly. Model predictive control of nonlinear systems: Computational burden and stability. IEE Proceedings — Control Theory and Applications, vol. 147, no. 4, pp. 387–394, 2000.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, LE11 3TU, UK

    Cunjia Liu & Wen-Hua Chen

  2. Nottingham Transportation Engineering Centre, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK

    John Andrews

Authors
  1. Cunjia Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen-Hua Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Andrews
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cunjia Liu.

Additional information

Cunjia Liu received the B.Eng. degree in detection, guidance, and control technology in 2005, the M. Eng. degree in guidance, navigation, and control in 2008 from Beijing University of Aeronautics and Astronautics, Beijing, PRC. He currently is a Ph. D. candidate in the Department of Aeronautical and Automotive Engineering at Loughborough University, UK.

His research interests include the optimization based control, flight control, and path planning of unmanned aerial vehicles.

Wen-Hua Chen received the M. Sc. and Ph.D. degrees from Department of Automatic Control at Northeast University, PRC in 1989 and 1991, respectively. From 1991 to 1996, he was a lecturer in Department of Automatic Control at Nanjing University of Aeronautics and Astronautics, PRC. He held a research position and then a lecturer in control engineering in Center for Systems and Control at University of Glasgow, UK from 1997 to 2000. He currently is a senior lecturer in flight control systems in Department of Aeronautical and Automotive Engineering at Loughborough University, UK.

His research interests include the development of advanced control strategies and their applications in aerospace engineering.

John Andrews is the Royal Academy of Engineering Professor of Infrastructure Asset Management at the Nottingham Transportation Engineering Centre (NTEC) at University of Nottingham, UK. Prior to this, he spent 20 years at Loughborough University, UK. The prime focus of his research has been on methods for predicting system reliability in terms of the component failure probabilities and a representation of the system structure. He is founding editor of the Journal of Risk and Reliability (Part O of the IMechE Proceedings). He is also a member of the editorial boards for Reliability Engineering and System Safety, and Quality and Reliability Engineering International.

His research interests include the fault tree technique and the use of the binary decision diagrams (BDDs) as an efficient and accurate solution method.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, C., Chen, WH. & Andrews, J. Experimental tests of autonomous ground vehicles with preview. Int. J. Autom. Comput. 7, 342–348 (2010). https://doi.org/10.1007/s11633-010-0513-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-010-0513-9

Keywords

Search

Navigation