Efficient bitmap-based implementation of sequential framework for motion planning for manipulators with many degrees of freedom
Bibliographic record
Abstract
Our sequential approach is a framework for developing practical motion planners for manipulators with many degrees-of-freedom (DOFs). The essence of this approach is to exploit the serial structure of manipulator arms and decompose the m-dimensional problem of planning collision-free motions for an n-link manipulator into a sequence of smaller m-dimensional subproblems, each of which corresponds to planning the motion of a subgroup of m-1 links along a given path. In this paper, we present an efficient bitmap-based implementation of the sequential framework. This bitmap-based implementation is more efficient and robust than a previously reported visibility graph-based implementation; and it utilizes a novel and more efficient backtracking mechanism. Our implementations are in C running on a SUN Sparc 10. We have conducted extensive experiments for planar arms with up to 8 degrees of freedom among randomly placed obstacles. The experiments show that the bitmap-based implementation of the sequential framework with the novel backtracking mechanism is very efficient. The average run time for a six degree of freedom manipulator in quite cluttered environments are around seven minutes. The planner succeeds for 100% of the examples in our simulations with small backtracking levels (2 for 4 DOF arm, 3 for 6 and 8 DOF arms).>
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".