Obstacle count independent real-time collision avoidance
Bibliographic record
Abstract
Robotic manipulator real-time collision avoidance is a safety critical mode of teleoperation where motion commands which would result in a collision are disallowed. To achieve real-time performance, it is necessary to efficiently detect impending collisions between the manipulator and the workspace obstacles. A collision detection method is presented which is based upon two representations. The dynamic elements, such as the manipulator links, are modelled as sets of spheres. The static elements, such as the workspace obstacles, are represented as a weighted voxel map, in which the value of any voxel is indicative of its distance to the nearest obstacle. Combining these two representations results in a collision detection method which is obstacle count independent, i.e. independent of the number of obstacles in the workspace. This property is desirable for operation in cluttered environments with many obstacles, where the total number of calculations in the alternative collision detection paradigm of pairwise comparison will prohibit real-time performance. The method is efficient enough to satisfy a hard real-time constraint Novel algorithms are described to generate the voxel map and spherical model representations, and an implementation is described which uses the collision detection method for real-time teleoperated collision avoidance and online path planning of a Puma 560 manipulator.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".