Machine Learning-based Framework for Optimally Solving the Analytical Inverse Kinematics for Redundant Manipulators

TL;DR

This paper introduces a neural network-based real-time framework for solving the inverse kinematics of redundant manipulators, optimizing for manipulability and configuration closeness to improve robustness in dynamic tasks.

Contribution

It presents a novel approach that directly learns redundancy parameters using neural networks for optimal IK solutions, enabling real-time performance and improved task robustness.

Findings

Achieves real-time IK computation in approximately 32 microseconds.

Demonstrates high accuracy and effectiveness through Monte Carlo simulations.

Outperforms traditional methods in dynamic, high-speed tasks.

Abstract

Solving the analytical inverse kinematics (IK) of redundant manipulators in real time is a difficult problem in robotics since its solution for a given target pose is not unique. Moreover, choosing the optimal IK solution with respect to application-specific demands helps to improve the robustness and to increase the success rate when driving the manipulator from its current configuration towards a desired pose. This is necessary, especially in high-dynamic tasks like catching objects in mid-flights. To compute a suitable target configuration in the joint space for a given target pose in the trajectory planning context, various factors such as travel time or manipulability must be considered. However, these factors increase the complexity of the overall problem which impedes real-time implementation. In this paper, a real-time framework to compute the analytical inverse kinematics of a redundant robot is presented. To this end, the analytical IK of the redundant manipulator is parameterized by so-called redundancy parameters, which are combined with a target pose to yield a unique IK solution. Most existing works in the literature either try to approximate the direct mapping from the desired pose of the manipulator to the solution of the IK or cluster the entire workspace to find IK solutions. In contrast, the proposed framework directly learns these redundancy parameters by using a neural network (NN) that provides the optimal IK solution with respect to the manipulability and the closeness to the current robot configuration. Monte Carlo simulations show the effectiveness of the proposed approach which is accurate and real-time capable ($\approx$ \SI{32}{\micro\second}) on the KUKA LBR iiwa 14 R820.