Sampling based Constrained Motion Planning For Floating Base Manipulators Using Constraints Driven Alternative Parameterization
Bibliographic record
Abstract
Constrained end-effector manipulation is in big demand in many robotic applications. We present a novel end-effector-constrained motion planning technique for floating-base manipulators. The proposed planner is superior to that cited in literature in terms of accuracy and execution time. The analytic comparison with the former approaches are given as well as simulation results for an end-effector trajectory following task. The strength of the proposed algorithm roots from the use of an alternative parametrization of the configuration space, where end-effector pose is included in this alternative parameterization space explicitly. This makes it possible to successfully generate constrained configuration samples with a uniform distribution. The proposed algorithm is based on RRT. We present a variation of the sample generation and steering routines which are suitable for end-effector constrained planning using the proposed alternative parameterization. Also, we present an asymptotically optimal version of our planner based on RRT* via simulations for end-effector trajectory tracking in presence of physical 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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".