Solving the Inverse Kinematics in Humanoid Robots: A Neural Approach

In this paper a method for solving the inverse kinematics of an humanoid robot based on artificial neural networks is presented. The input of the network is the desired positions and orientations of one foot with respect to the other foot. The output is the joint coordinates that make it possible to reach the goal configuration of the robot leg. To get a good set of sample data to train the neural network the direct kinematics of the robot needs to be developed, so to formulate the relationship between the joint variables and the position and orientation of the robot. Once this goal has been achieved, we need to establish the criteria we are going to use to choose from the range of possible joint configurations that fit with a particular foot position of the robot. These criteria will be used to filter all the possible configurations and retain the ones that make the robot configurations more stable in the training set.