Towards Comparison and Real Time Implementation of Path Planning Methods for 2R Planar Manipulator with Obstacles Avoidance
Bibliographic record
Abstract
The main requirement of parametric path planning techniques in robot manipulators is to create continuous, smooth, and easy-to-modify path such as to move the end-effector from start point to destination point. To meet these requirements, the rational Bezier and NURBS algorithms have been proposed for path planning of 2R manipulator in environment with known and static obstacles. In this study, a comparison in terms of path length and time consumption of algorithm has been conducted to show the superior of one method to another. Based on numerical simulation, it has been shown that the rational Bezier algorithm generates shorter path and takes less time to complete the path planning task as compared to NURBS method. In addition, this study presented the design of real-time set-up based on Arduino UNO microcontroller and micro-stepping actuators. It has been shown that the experimental results could successfully verify the numerical results for both proposed path planning methods for different configuration of obstacles.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".