Design and Build of a Microcontroller Robot Arm with Smartphone Control Based on the Internet of Things
Bibliographic record
Abstract
This research discusses the design and implementation of a robot arm that is controlled via smartphone using the Internet of Things (IoT) concept. This robotic arm is controlled using a microcontroller connected to the internet. The aim of this research is to develop a system that allows users to easily control the movement of the robot arm via a specially designed smartphone application. At the design stage, the microcontroller is programmed to control the motors that drive the robotic arm joints. Communication between the smartphone and the robot arm is implemented via a network communication protocol, so the user can give commands via an intuitive application interface. The use of IoT technology allows this robot arm to be controlled remotely via the internet, opening opportunities for use in various contexts, such as use in production, education or even entertainment environments. Test results show that the robotic arm can automatically carry out repetitive tasks with a high level of accuracy. This advantage can increase productivity in the production process and reduce the potential for human error. In addition, robots can operate in environments that are potentially dangerous to humans, such as toxic, radioactive or extreme temperature (hot or cold) areas. This capability effectively reduces the risk of human exposure to these potential hazards
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".