A Streamlined Search Algorithm for Path Modifications of a Safe Robot Manipulator<sup>*</sup>
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In recent years, the interest in human-robot interactions has added a new dimension to the on-line path planning problem by requiring a method that guarantees a risk-free path. This paper presents a streamlined search algorithm for fast path modification. The algorithm is formulated as an optimization problem that evaluates alternative paths nearby each obstacle. Each path is evaluated based on the value of the danger assigned to that path. To reduce the size of the search space, the minimum number of via points necessary to alter the path is initially obtained using a geometrical method. Given the number of via points, the algorithm proceeds to locate the via points around the obstacle such that the resulting path through these via points satisfies all problem constraints. Obtaining a solution in this way renders a fast algorithm for path modification, while it better avoids problems often encountered in other gradient-based search algorithms. Case studies for two planar robots are provided to highlight some of the advantages of the proposed algorithm. Experimental results using a CRS-F3 robot manipulator validate the effectiveness of the algorithm for applications involving human-robot interactions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it