Efficient bitmap-based implementation of sequential framework for motion planning for manipulators with many degrees of freedom
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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).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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