Fuzzy systems to solve inverse kinematics problem in robots control: application to an hexapod robots´ leg

The complexity of walking robots poses a number of control problems due to the large number of degrees of freedom involved in the robot´s motion. This work deals with the design of a hexapod robot, whose legs have 3 rotative joints and the same configuration. The kinematics analysis of one leg is presented and used to generate data for black box identification using fuzzy systems and neural networks: the direct kinematics is used to generate the training data and the inverse kinematics is used to generate the testing data. Two fuzzy systems and a neural network are used as general black box methods to solve the inverse kinematics problem in robots´ leg control. Results have shown that reasonable precision can be achieved with low computational cost