Self-organizing feature maps for modeling and control of robotic manipulators
This paper presents a review of self-organizing feature maps (SOFMs), in particular, those based on the Kohonen algorithm, applied to adaptive modeling and control of robotic manipulators. Through a number of references we show how SOFMs can learn nonlinear input-output mappings needed to control robotic manipulators, thereby coping with important robotic issues such as the excess degrees of freedom, computation of inverse kinematics and dynamics, hand-eye coordination, path-planning, obstacle avoidance, and compliant motion. We conclude the paper arguing that SOFMs can be a much simpler, feasible alternative to MLP and RBF networks for function approximation and for the design of neurocontrollers. Comparison with other supervised/unsupervised approaches and directions for further work on the field are also provided.
self-organizing feature maps robotics kinematics and dynamics hand-eye coordination obstacle avoidance compliant motion path planning redundancy