An Overview of Scientometric Mapping in Planning and Decision-Making for UGV
Bibliographic record
Abstract
The literature on planning and decision-making for Unmanned Ground Vehicle (UGV) research has grown rapidly over the last decade, but few attempts have been made to map global research in this area. In order to determine the state of the field and trends in planning and decision-making for UGV research, this study used scientometric mapping software to conduct a series of content analyses. In this study, we used a series of content analyses and investigated global patterns of publications, including distribution of institutions, common categories, highly cited papers and research hotspots, as well as trends in co-citation analysis and, in particular, cluster analysis. We present all the main aspects of the study in a scientific-metrical way and draw the following conclusions. Firstly, the most important advances and developments in planning and decision-making for UGV have taken place mainly in China, the USA and Germany. The three countries, China, the USA and Canada, have a relatively close scientific cooperation in this field. Secondly, Chinese universities publish a high number of articles and American universities publish high impact articles on the whole. Thirdly, the emerging trends in research on planning and decision-making for UGV research have shifted from traditional planning methods, such as rule-based decision-making system, FSM, hybrid-state A* path planner to methods integrated with perception and planning, such as end-to-end learning method.
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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.082 | 0.154 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".