Exploring Analytical and Deep Learning Solutions for High-Degree-of-Freedom Inverse Kinematics
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".