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Exploring Analytical and Deep Learning Solutions for High-Degree-of-Freedom Inverse Kinematics

S. Kalaycioglu, Anton de Ruiter, Enrica Fung, Hongsheng Zhang, Hua Xie

Year
2025
Citations
2

Abstract

Abstract This paper presents a comparative study of analytical and deep learning (DL) approaches for solving the inverse kinematics (IK) problem in high-degree-of-freedom (DOF) robotic manipulators, focusing on the Lunar Exploration Rover System (LERS). The IK problem, central to robotic motion control, is traditionally solved using analytical methods, but recent advances in deep learning provide new opportunities to enhance precision and efficiency. The study evaluates the performance of DL Neural Networks, in addressing the complexities of high-DOF manipulators. A detailed comparison is conducted between traditional geometric solutions and DL-based models, with an emphasis on robustness and computational efficiency under noisy conditions. The results demonstrate the potential of DL methods to outperform traditional techniques in high-DOF environments, paving the way for future advancements in autonomous robotic systems. In addition to the IK study, the paper discusses the design and integration of the LERS robotic system, which plays a critical role in advancing autonomous lunar exploration under the ARTEMIS program. As part of this international collaboration involving NASA, the Canadian Space Agency (CSA), the European Space Agency (ESA), and the Japan Aerospace Exploration Agency (JAXA), LERS is tasked with supporting both crewed and unmanned missions on the Moon. LERS is designed to perform precise robotic manipulation tasks such as deploying infrastructure, gathering scientific data, and managing lunar resources, all of which are vital for future missions to Mars. The implementation of advanced IK solutions is key to enabling the precise control of LERS’s robotic arms, allowing for the successful execution of complex tasks such as assembling habitats, handling materials, and conducting scientific analyses on the lunar surface. This work highlights the importance of IK in ensuring that robotic systems like LERS can operate with the precision needed for the next generation of lunar missions.

Keywords

Degree (music)KinematicsInverse kinematicsInverseArtificial intelligenceComputer scienceMathematicsPhysicsClassical mechanicsGeometry

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