Formation Control Design for Remote Field Monitoring with IoT-enabled Mobile Robots
Bibliographic record
Abstract
Incorporating technological advancements in the farming industry is a growing trend due to the increasing demand for production and resources in agricultural services. With the significant global population increasing yearly, farming services need improvements to cater to it. One approach the farming industry considers is utilizing the Internet of Things (IoT) networks and robotic control systems for better efficiency and sustainability. IoT technology, with its sensors and network devices, allows wireless communication for agricultural services to expand the coverage of their techniques and implement more convenient ways of data sensing and real-time analysis. Also, they introduce control systems and robotics to automate their tasks and procedures, making each service more sustainable and efficient. So, in this work, we take these two and propose an IoT-enabled formation control design to use the strengths of these technologies and present a viable design for more effective and sustainable remote field monitoring. With our IoT network arrangement with the cloud and its gateways, we present a means to expand the reach of the field monitoring service and its coverage to span wider farmlands. In addition, we performed simulation studies that demonstrate the feasibility of our proposed formation tracking control strategy with proven stability. Overall, we present a feasible approach for a more efficient and sustainable remote field monitoring system for farming services using a formation control design with IoTenabled mobile 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".