Energy Saving in an Autonomous Excavator via Parallel Actuators Design and PSO-Based Excavation Path Generation
Bibliographic record
Abstract
An autonomous excavator can be a good solution in the construction industry to deal with existing issues such as high labor costs and harsh and hazardous environmental conditions. To increase energy efficiency for autonomous excavators, this study proposes two approaches. First, a new and unique design with parallel arm and bucket actuators is proposed for an electric excavator manipulator. Since the three actuators of the boom, arm, and bucket are in series for the conventional design of excavators, it is difficult to share external loads between them. However, a parallel configuration of the arm and bucket actuators in the proposed new design can facilitate load sharing and overcome higher external loads. By replacing hydraulic actuators with electric linear actuators, this design also reduces energy consumption during idling. Moreover, with low back drivability, the electric linear actuators can handle relatively high external forces without spending energy while not in motion. Secondly, a PSO-based path-generation algorithm was developed for autonomous excavation to minimize energy consumption while avoiding collisions with unwanted obstacles. In the PSO algorithm, it is possible to change the priorities of the elements to the minimum by adjusting the gains in the cost function. Two scenarios—scenarios with and without considering energy saving—were considered to test the performance of the developed algorithm, with the results between the scenarios compared. Simulation results show that the proposed algorithm reduces energy consumption in each digging cycle by 18.51%.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".