Investigating the Design and Application of Mobile Robotic Devices with Manipulation Devices for Space Technology
Bibliographic record
Abstract
The subject's relevance stems from the rapid advancement of science and technology, which drives the extensive utilization of controlled mobile robot models with manipulators across diverse domains.The study aims to explore the development of versatile mobile robotic devices using manipulators for space applications, drawing from the expertise of specialists at Almaty University of Power Engineering and Telecommunications.The methodological approach is based on a combination of methods of system analysis of the principles of building a highly functional model of a mobile robot made based on manipulation devices, with an analytical study of the main directions of using mobile robots in the space sphere.The study underscores designing space mobile robots based on human body dynamics, stressing the necessity of robust mathematical models and specialized software, meeting stringent reliability, simplicity, and safety criteria.The findings underscored the critical role of manipulator-based mobile robot modeling in space technology development, offering practical insights for developers in various technological fields, including aerospace.The practical implications of the research include the possibility of developing and implementing reliable and efficient manipulative mobile robots for use in space, which will help to increase the efficiency of space operations and reduce risks to humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".