Wheel-Legged Robotic Limb to Assist Human With Load Carriage: An Application For Environmental Disinfection During COVID-19
Bibliographic record
Abstract
During COVID-19, with a heavy sprayer filled with disinfectant, the risk of infection for epidemic prevention personnel has been increased by long-term environmental disinfection. In order to reduce the burden and save energy of human, this letter proposed a Wheel-Legged Robotic Limb (WRL) for the carriers. The mass of WRL is only 1.77 kg. The WRL has one rigid robotic limb located below the sprayer, which can provide active supporting force for the sprayer. The WRL adopts force closed-loop control method to ensure the system provide an expected supporting force. The system performance was evaluated including standing and walking at 5 km/h, under three experimental conditions included: 1) with a sprayer, 19.41 kg (SPRAYER), 2) with the powered WRL, 22.18 kg (WRL_ON), and 3) with the unpowered WRL, 22.18 kg (WRL_OFF). When the supporting force is set as 80 N, the experimental results show that the WRL_ON condition has reduced the vertical load force on the human, the vertical ground reaction force of human feet, and the metabolic power by 41.28%, 8.03%, and 17.46% during standing, and also reduced by 32.29%, 8.08% and 18.92% during walking, compared to SPRAYERcondition, respectively.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".