articles

A method for solving the inverse kinematics problem for a three-link robotic manipulator is proposed based on one of the types of machine learning - reinforcement learning. In the general case, this task consists of finding the laws of change in the generalized coordinates of the manipulator’s gripping device that provide the specified kinematic parameters. When solving the problem analytically, the basis for calculating inverse kinematics is the Denavit – Hartenberg parameters with further implementation of numerical matrix calculations. However, taking into account the kinematic redundancy of multi-link manipulators, this approach is labor-intensive and does not allow automated consideration of changes in the external environment in real time, as well as the features of the robot’s field of application. Therefore, an urgent research task is to develop a solution whose structure contains a self-learning block that provides a solution to the inverse kinematics problem under conditions of a changing external environment, the behavior of which is unknown in advance. The proposed method is based on simulating the process of achieving the goal of robot control (positioning the gripping device of the manipulator) at a given point in space using the trial and error method. For approaching the goal at each learning step, a reward function is calculated, which is used when controlling the robot. In the proposed method, the agent is a recurrent artificial neural network, and the environment, the state of which is observed and assessed, is a robotic manipulator. The use of a recurrent neural network made it possible to take into account the history of the movement of the manipulator and overcome the difficulties associated with the fact that different combinations of angles between links can lead to the same point in the workspace. Testing of the proposed method was carried out on a virtual model of the robot, made using the MatLAB Robotics System Toolbox and the Simscape environment, which showed high efficiency in terms of the “time – accuracy” criterion of the proposed method for solving the inverse kinematics problem.