Adaptive Manipulability-Based Path Planning Strategy for Industrial Robot Manipulators
Bibliographic record
Abstract
In this article, a novel manipulability-based optimal rapidly exploring random tree (RRT*) path planning strategy is proposed for industrial robot manipulators. When sampling in the search space, two constraints, namely, path length and manipulability measure, are imposed to find a minimal-cost path connecting the start and goal points. By tracking the generated path, a robot manipulator's end-effector can traverse the workspace with a shorter length and, meanwhile, avoid configuration singularities. A constrained closed-loop inverse kinematics technique is utilized to exploit the kinematic redundancy to assign a higher manipulability to an end-effector position. Additionally, the metrics of path length and manipulability measure are used to determine the adaptive step size for the RRT* planner. This helps the space-filling tree to grow efficiently toward unsearched areas and find an optimal path. Simulation analysis and experimental results of a six-degree-of-freedom FANUC-M-20iA industrial robot illustrate the efficiency of the proposed path planning methods.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".