Automatic robotic recharging systems – development and challenges
Bibliographic record
Abstract
Purpose Since the market penetrations of service robots are only successful to a limited types of services, the purpose of the paper is to look into the reasons why the market penetrations are lagged from both technical and nontechnical perspectives. Automatic robotic recharging services, especially robotic refueling systems, are used as the case study for the investigation. Design/methodology/approach This paper surveyed the relevant technologies and products and conducted the feasibility study and risk management for new development of automated robotic refueling systems. This paper developed a cost model for the evaluation of robotic refueling systems. Findings There are no major technical barriers that exist for the development of robotic refueling systems, but two main risks of developing new robotic refueling systems are interference of existing patents and the extreme effort to further reduce the development cost of automated refueling systems. The recommendations have been made to new developers of service robots. Research limitations/implications The suggestions are made for further development on service robots, in general; however, this paper does not cover the physical development of service robots. Practical implications This study was actually conducted for a client company who has a strong interest in developing new products for automatic robotic refueling systems. The reported work has great significance for new comers in this area to understand the state of the art, technological challenges and some potential risks in the field. Originality/value To the best of authors’ knowledge, it will be the first academic paper to summarize the research and development effort on automatic recharging business. The targeted field is very typical in promoting robots in services. Even robotic refueling was proposed at very early stage of robotic application, the market penetration of refueling robots the market penetration is very limited, not because the technology readiness but some other factors. This work has its significance to identify technical and nontechnical challenges to promote robots in services.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".