Advertisement

Adaptive Control of Human-Interacted Mobile Robots with Velocity Constraint

  • Qing XuEmail author
  • Shuzhi Sam Ge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11357)

Abstract

In this paper, we present an adaptive control for mobile robots moving in human environments with velocity constraints. The mobile robot is commanded to track the desired trajectory while at the same time guarantee the satisfaction of the velocity constraints. Neural networks are constructed to deal with unstructured and unmodeled dynamic nonlinearities. Lyapunov function is employed during the course of control design to implement the validness of the proposed approach. The effectiveness of the proposed framework is verified through simulation studies.

Keywords

Robot-human interaction Motion constraints Neural network control 

References

  1. 1.
    Fierro, R., Lewis, F.L.: Control of a nonholonomic mobile robot using neural networks. IEEE Trans. Neural Netw. 9(4), 589–600 (1998)CrossRefGoogle Scholar
  2. 2.
    Ge, S.S., Hang, C.C., Lee, T.H., Zhang, T.: Stable Adaptive Neural Network Control. Springer, Boston (2013).  https://doi.org/10.1007/978-1-4757-6577-9CrossRefzbMATHGoogle Scholar
  3. 3.
    He, W., Dong, Y.: Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Trans. Neural Netw. Learn. Syst. 29(4), 1174–1186 (2017)CrossRefGoogle Scholar
  4. 4.
    Lee, D., Liu, C., Liao, Y.W., Hedrick, J.K.: Parallel interacting multiple model-based human motion prediction for motion planning of companion robots. IEEE Trans. Autom. Sci. Eng. 14(1), 52–61 (2016)CrossRefGoogle Scholar
  5. 5.
    Shiomi, M., Zanlungo, F., Hayashi, K., Kanda, T.: Towards a socially acceptable collision avoidance for a mobile robot navigating among pedestrians using a pedestrian model. Int. J. Soc. Robot. 6(3), 443–455 (2014)CrossRefGoogle Scholar
  6. 6.
    Teatro, T.A.V., Eklund, J.M., Milman, R.: Nonlinear model predictive control for omnidirectional robot motion planning and tracking with avoidance of moving obstacles. Can. J. Electr. Comput. Eng. 37(3), 151–156 (2014)CrossRefGoogle Scholar
  7. 7.
    Ton, C., Kan, Z., Mehta, S.S.: Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields. Robotica 36(4), 463–483 (2017)CrossRefGoogle Scholar
  8. 8.
    Wang, C., Li, Y., Ge, S.S., Tong, H.L.: Adaptive control for robot navigation in human environments based on social force model. In: IEEE International Conference on Robotics and Automation, pp. 5690–5695 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.The School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.The Department of Electrical and Computer Engineering, and the Social Robotics Lab, Interactive and Digital Media Institute (IDMI)National University of SingaporeSingaporeSingapore

Personalised recommendations