Design and Implementation of an Adaptive Neuro-controller for Trajectory Tracking of Nonholonomic Wheeled Mobile Robots

  • Francisco García-Córdova
  • Antonio Guerrero-González
  • Fulgencio Marín-García
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)


A kinematic adaptive neuro-controller for trajectory tracking of nonholonomic mobile robots is proposed. The kinematic adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is termed Self-Organization Direction Mapping Network (SODMN), and combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates. The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot’s sensors. The efficacy of the proposed neural architecture is tested experimentally by a differentially driven mobile robot.


Mobile Robot Direction Mapping Trajectory Tracking Neural Architecture Driving Wheel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Francisco García-Córdova
    • 1
  • Antonio Guerrero-González
    • 1
  • Fulgencio Marín-García
    • 2
  1. 1.Department of System Engineering and Automation 
  2. 2.Department of Electrical Engineering, Polytechnic University of Cartagena (UPCT), Campus Muralla del Mar, 30202, Cartagena, MurciaSpain

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