Skip to main content

Advertisement

SpringerLink
Log in

Evolution of Agent, Remotely Operating a Scale Model of a Car Through a Latent Video Feedback

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

We present an evolution of an agent, remotely operating a fast running scale model of a car. The agent perceives the environment from overhead video camera and conveys its actions via radio control transmitter. In order to cope with the video feed latency we propose an anticipatory modeling in which the agent considers its actions based on the anticipated state of the car. The agent is first evolved offline on a software simulator and then adapted online to the real world. During the online evolution, the lap times improve to the values much close to the values obtained from the offline evolution. An online evolutionary optimization of the avoidance of a small static obstacle with a priori known properties results in lap times that are virtually the same as the best lap times achieved on the same track without obstacles. This work can be viewed as a step towards the automated design of controllers of remotely operated vehicles capable to find an optimal solution to various tasks in a priori known environments.

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. Abbott, E., Powell, D.: Land-vehicle navigation using GPS. Proc. I.E.E.E 87(1), 145–162 (1999)

    Google Scholar 

  2. Angeline, P. J.: Genetic programming and emergent intelligence. In: Kinnear Jr., K. E. (ed.) Advances in Genetic Programming, pp. 75–98. MIT, Cambridge, MA (1994)

    Google Scholar 

  3. Arcara, P., Melchiorri, C.: Control schemes for teleoperation with time delay: a comparative study. Robot. Auton. Syst. 38(1), 49–64 (2002)

    Article  MATH  Google Scholar 

  4. Bentley, B.: Speed Secrets: Professional Race Driving Techniques. Motorbooks International, Osceola, WI (1998)

    Google Scholar 

  5. Chaperot, B., Fyfe, C.: Improving Artificial Intelligence in a Motocross Game. IEEE Symposium on Computational Intelligence and Games, pp.1435–1500. CIG’06, May (2006)

  6. Frere, P.: Sports Cars and Competition Driving. Bentley, Sudbury, MA (1992)

    Google Scholar 

  7. Funge, J.D.: Artificial Intelligence for Computer Games. AK Peters, Wellesley, MA (2004)

    Google Scholar 

  8. Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison–Wesley, Reading, MA (1989)

    MATH  Google Scholar 

  9. IBM Corporation. Deep Blue. http://www.research.ibm.com/deepblue/ (1997)

  10. Jacobi, N.: Minimal simulations for evolutionary robotics. Ph.D. thesis, School of Cognitive and Computing Sciences, Sussex University (1998)

  11. Kalman, R.E.: A new approach to linear filtering and prediction problems. J Basic Eng 82, 35–45 (1960)

    Google Scholar 

  12. Lane, J.C., Carignan, C., Akin, D.: Time delay and communication bandwidth limitation on telerobotic control. In: Proc. SPIE Mobile Robots XV and Telemanipulator and Telepresence Technologies VII, 405–419 (2001)

  13. Meeden, L., Kumar, D.: Trends in evolutionary robotics. In: Jain, L.C., Fukuda, T. (eds.) Soft Computing for Intelligent Robotic Systems, pp. 215–233. Physica, New York, NY (1998)

    Google Scholar 

  14. Miller, B.L., Goldberg, D.E.: Genetic algorithms, tournament selection, and the effects of noise. Complex Syst. 9(3), 193–212 (1995)

    MathSciNet  Google Scholar 

  15. Montana, D.: Strongly typed genetic programming. Evol. Comput. 3(2), 199–230 (1995)

    Article  Google Scholar 

  16. Niemeyer, G., Slotine, J.-J.E.: Telemanipulation with time delays. Int. J. Rob. Res. 23, 873–890 (2004). (9)

    Article  Google Scholar 

  17. Robocup (2005) http://www.robocup.org/02.html

  18. Rosen, R.: Anticipatory Systems. Pergamon, New York (1985)

    Google Scholar 

  19. Togelius J., Lucas S.M.: Evolving Controllers for Simulated Car Racing. In: Proceedings of IEEE Congress on Evolutionary Computations (CEC-2005). 1906–1913, Edinburgh, UK, September 2–5 (2005)

  20. Tanev, I., Joachimczak, M., Hemmi H., Shimohara, K.: Evolution of the Driving Styles of Anticipatory Agent Remotely Operating a Scaled Model of Racing Car. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC-2005), 1891–1898, Edinburgh, UK, September 2–5 (2005)

  21. Tanev, I., Shimohara, K.: On human competitiveness of the evolved agent operating a scale model of a car. In: IEEE Congress on Evolutionary Computation (CEC-2007), pp. 3646–3653, Singapore, September 25–28 (2007)

  22. Wloch, K., Bentley, P.: Optimizing the performance of a formula one car using a genetic algorithm. In: Proceedings of the 8th International Conference on Parallel Problem Solving from Nature, pp. 702–711. Birmingham, UK, September 18–22 (2004)

Download references

Author information

Authors and Affiliations

  1. Department of Information Systems Design, Faculty of Engineering, Doshisha University, 1-3 Miyakodani, Tatara, Kyotanabe, Kyoto, 610-0321, Japan

    Ivan Tanev & Katsunori Shimohara

Authors
  1. Ivan Tanev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katsunori Shimohara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ivan Tanev.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tanev, I., Shimohara, K. Evolution of Agent, Remotely Operating a Scale Model of a Car Through a Latent Video Feedback. J Intell Robot Syst 52, 263–283 (2008). https://doi.org/10.1007/s10846-008-9212-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-008-9212-y

Keywords

Search

Navigation