Learning of operator hand movements via least angle regression to be teached in a manipulator

In this document, a control system is developed to allow a manipulator to learn and plan references from demonstrations given by an operator hand. Data entry is acquired by a sensor and is learned by the generalized learning model with least angle regression to create a desired reference in three dimensional space. A fifth reference profile is employed to smooth the desired reference. Direct and inverse kinematics are gotten to represent the transformation between the three dimensional space and each of the manipulator links. A dynamic model is gotten using Newton–Euler formulation. An evolving proportional derivative (PD) control is applied to get that the manipulator end effector follows the operator hand movements. The monitoring and control systems are implemented in an embedded platform for testing purposes.