Artificial Intelligence Methods in Reactive Navigation of Mobile Robots Formation

Zenon Hendzel, Marcin Szuster, Andrzej Burghardt

2012

Abstract

The article presents a hierarchical control system build using artificial intelligence methods, that generates a trajectory of the wheeled mobile robots formation, and realises the tracking control task of all agents. The hierarchical control system consists of a navigator, based on a conception of behavioural control signals coordination, and individual tracking control systems for all mobile robots in the formation. The navigator realises a sensor-based approach to the path planning process in the unknown 2-D environment with static obstacles. The navigator presents a new approach to the behavioural control, where one Neural dynamic programming algorithm generates the control signal for the complex behaviour, which is a composition of two individual behaviours: “goal-seeking”and “obstacle avoiding”. Influence of individual behaviours on the navigator control signal depends on the environment conditions and changes fluently. On the basis of control signal generated by the navigator are computed the desired collision-free trajectories for all robots in formation, realised by the tracking control systems. Realisation of generated trajectories guarantees reaching the goal by selected point of the robots formation with obstacles avoiding by all agents. Computer simulations have been conducted to illustrate the process of path planning in the different environment conditions.

References

  1. A. Burghardt, T. B. and Giergiel, J. (2011). Control of robots' formation in unknown surroundings environment. In Dynamical Systems. Nonlinear dynamic and control, Proc. of Conference on Dynamical Systems, Theory and Applications. WPL.
  2. Arkin, R. (1998). Behavior-Based Robotics. Intelligent Robots and Autonomous Agents. MIT Press.
  3. Burghardt, A. (2004). Behavioural control of wheeled minirobot, (in polish). PAK, 11:26-29.
  4. Burghardt, A. (2008). Proposal for a rapid prototyping environment for algorithms intended for autonomous mobile robot control. Mechanics and Mechanical Engineering, 12:5-16.
  5. Egerstedt, M. and Hu, X. (2001). Formation constrained multi-agent control. IEEE Transactions on Robotics and Automation, 17(6):947-951.
  6. Fahimi, F. (2008). Autonomous Robots: Modeling, Path Planning, and Control. Springer.
  7. Giergiel, J. and Zylski, W. (2005). Description of motion of a mobile robot by maggie's equations. J. Theor. App. Mech., 43:511-521.
  8. Giergiel J., Hendzel Z., Z. W. (2002). Modeling and control of wheeled mobile robots (in Polish). PWN.
  9. Hendzel, Z. (2004). Fuzzy reactive control of wheeled mobile robot. J. Theor. App. Mech., 42:503-517.
  10. Hendzel, Z. and Szuster, M. (2010a). Discrete action dependant heuristic dynamic programming in wheeled mobile robot control. Solid State Phenomena, 164:419- 424.
  11. Hendzel, Z. and Szuster, M. (2010b). Discrete model-based adaptive critic designs in wheeled mobile robot control. In Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II, ICAISC'10, pages 264-271, Berlin, Heidelberg. Springer-Verlag.
  12. Hendzel, Z. and Szuster, M. (2011). Neural dynamic programming in behavioural control of wheeled mobile robot (in polish). Acta Mechanica et Automatica, 5(1):28-36.
  13. Hendzel, Z. and Szuster, M. (2012). Neural dynamic programming in reactive navigation of wheeled mobile robot. LNCS, 7268:450-457.
  14. Maaref, H. and Barret, C. (2002). Sensor-based navigation of a mobile robot in an indoor environment. Robotics and Autonomous Systems, 38(1):1-18.
  15. Millan, J. D. (1995). Reinforcement learning of goaldirected obstacle-avoiding reaction strategies in an autonomous mobile robot. Robotics and Autonomous Systems, 15:237-246.
  16. Ogren, P. and Leonard, N. (2003). Obstacle avoidance in formation. Proceedings of 2003 ICRA.
  17. Powell, W. B. (2007). Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics). Wiley-Interscience.
  18. Prokhorov, D. V. and Wunsch, D. C. (1997). Adaptive critic designs. IEEE Transactions on Neural Networks, 8(5):997-1007.
  19. Si, J., Barto, A. G., Powell, W. B., and Wunsch, D. (2004). Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence). Wiley-IEEE Press.
  20. Sutton, R. and Barto, A. (1998). Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learning. MIT Press.
  21. Yamaguchi, H. (1997). Adaptive formation control for distributed autonomous mobile robot groups. In Robotics and Automation, 1997. Proceedings., 1997 IEEE International Conference on, volume 3, pages 2300- 2305.
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Paper Citation


in Harvard Style

Hendzel Z., Szuster M. and Burghardt A. (2012). Artificial Intelligence Methods in Reactive Navigation of Mobile Robots Formation . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 466-473. DOI: 10.5220/0004113404660473


in Bibtex Style

@conference{ncta12,
author={Zenon Hendzel and Marcin Szuster and Andrzej Burghardt},
title={Artificial Intelligence Methods in Reactive Navigation of Mobile Robots Formation},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={466-473},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004113404660473},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - Artificial Intelligence Methods in Reactive Navigation of Mobile Robots Formation
SN - 978-989-8565-33-4
AU - Hendzel Z.
AU - Szuster M.
AU - Burghardt A.
PY - 2012
SP - 466
EP - 473
DO - 10.5220/0004113404660473