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.