Energy efficiency of mobile soft robots
Bibliographic record
Abstract
The performance of mobile soft robots is usually characterized by their locomotion/velocity efficiency, whereas the energy efficiency is a more intrinsic and fundamental criterion for the performance evaluation of independent or integrated soft robots. In this work, a general framework is established to evaluate the energy efficiency of mobile soft robots by considering the efficiency of the energy source, actuator and locomotion, and some insights for improving the efficiency of soft robotic systems are presented. Proposed as the ratio of the desired locomotion kinetic energy to the input mechanical energy, the energy efficiency of locomotion is found to play a critical role in determining the overall energy efficiency of soft robots. Four key factors related to the locomotion energy efficiency are identified, that is, the locomotion modes, material properties, geometric sizes, and actuation states. It is found that the energy efficiency of most mobile soft robots reported in the literature is surprisingly low (mostly below 0.1%), due to the inefficient mechanical energy that essentially does not contribute to the desired locomotion. A comparison of the locomotion energy efficiency for several representative locomotion modes in the literature is presented, showing a descending ranking as: jumping ≫ fish-like swimming > snake-like slithering > rolling > rising/turning over > inchworm-like inching > quadruped gait > earthworm-like squirming. Besides, considering the same locomotion mode, soft robots with lower stiffness, higher density and larger size tend to have higher locomotion energy efficiency. Moreover, a periodic pulse actuation instead of a continuous actuation mode may significantly reduce the input mechanical energy, thus improving the locomotion energy efficiency, especially when the pulse actuation matches the resonant states of the soft robots. The results presented herein indicate a large and necessary space for improving the locomotion energy efficiency, which is of practical significance for the future development and application of soft robots.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".