Real-Time Neural-Net Driven Optimized Inverse Kinematics for a Robotic Manipulator Arm

Abstract

This paper proposes a method that optimizes the inverse kinematics needed for the trajectory generation of a 4-DOF (degrees of freedom) robotic manipulator arm to give results in real time. Due to the many-to-one mapping of the angle vector which describes the position of the manipulator joints and to the coordinates of the end-effector, traditional methods fail to address the redundancy that exists in an efficient way. The proposed method is singular, and in that it (1) Generates the most optimal angle vector in terms of maximum manipulability, a factor which determines the ease with which the arm moves, for a given end-vector. (2) Proposes a novel approach to inculcate the complexity of dealing with real coordinate system by proposing a machine learning technique that uses neural networks to predict angle vector for practically any end-effector position although it learns on only a few sampled space. (3) Works in real time since the entire optimization of the manipulability measure are done offline before training the neural network using a relevant technique which makes the proposed method suitable for practical uses. (4) It also determines the shortest, smooth path along which the manipulator can move along avoiding any obstacles. To the best of the authors’ knowledge, this is the first neural-net-based optimized inverse kinematics method applied for a robotic manipulator arm, and its optimal and simple structure also makes it possible to run it on NVIDIA Jetson Nano Module.