Grid-to-Robot: Deep Wasserstein generative modeling of robot/power grid interaction using hybrid adversarial Residual Networks
Bibliographic record
Abstract
Smart Manufacturing (SM) is an important factor for driving innovation, enhancing operational efficiency, and increasing sustainable industrial growth in an increasingly competitive and resource-constrained world. However, it faces several challenges related to increasing energy consumption and climate change. The high energy demands of connected devices and robotic manipulators increase the carbon footprint. To resolve this issue, most enterprises are now transitioning to use Renewable Energy Sources (RES), and optimizing their power and energy usage, while holding the process efficient. To fully achieve this transition, a detailed power modeling of the robotic manufacturing system is crucial and, therefore, it is important to investigate this power modeling of the robotic manipulators’ consumption in a Smart Sustainable Manufacturing (SSM) to achieve the best power modeling results and better integrability analytics in optimal power planning of the robotic systems power supply. To this end, this paper presents a deep Generative Artificial Intelligence (GAI)-based modeling of robotic manipulators’ power supply interaction with the power grid, and RES. In the proposed system, which is powered by solar energy and the power grid, a SSM equipped with ten 6-Degrees of Freedom (DoF) robotic manipulators is considered in the presence of Battery Energy Storage Systems (BESSs). Subsequently, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is employed to generate synthetic data for the system alongside the real data, thereby expanding the analytical horizons across varying operational characteristics of the system. Following this, a Residual Networks (ResNet) is developed to comprehensively analyze and predictively model the power consumption of the manipulators and their interactions with the power supply resources. Finally, the proposed hybrid GAI modeling strategy is numerically evaluated across a broad spectrum of Key Performance Indicators (KPIs) (MSE= 1 0 − 4 , MAE= 3 . 6 × 1 0 − 3 , R 2 = 99.98%, MARE= 1 . 97 × 1 0 − 2 , RMSPE= 8 . 83 × 1 0 − 2 % , MSRE= 7 . 8 × 1 0 − 3 , RMSRE= 8 . 84 × 1 0 − 2 , MAPE= 1 . 97 × 1 0 − 2 % , and Max Error= 2 . 04 × 1 0 − 2 ), where these metrics demonstrate superior performance in power modeling. As a result, the concept of Grid-to-Robot (G2R) is introduced for the first time as a foundation for further advancements in SSM, enhancing sustainability and mitigating negative impacts on climate change while contributing to the development of an advanced manufacturing system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".