Multi-Agent Learning for the Inverse Kinematics of a Robotic Arm

This paper presents a solution to the inverse kinematics problem for robotic manipulator based on the Adaptive Multi-Agent System (AMAS) approach. In this research, multi-agent system is in charge of controlling a robot arm with four degrees of freedom (DOF) and two motorized wheels, giving appropriate commands, such as rotation angles and velocities, to reach the desired position and orientation of the end effector. The calculation of commands is directly related to the solving of forward and inverse kinematics. Before the learning process of AMOEBA, the rotational angles, θ values, are encoded into a single number N, this parameter is the desired value that we are going to predict in the predicting stage. During the learning phase, the Agnostic MOdEl Builder by self-Adaptation (AMOEBA) builds context agents, which has local models and is able to self-adapt. After the getting the predicted value, Npred, it will be decoded back to get the set of rotational angles that is given to robot end effector. In addition, the robot with all its physical parameters is modeled and simulated in the Robot Operating System (ROS) environment