Collision-free Traiectory Planning For Robot Manipulators†
Bibliographic record
Abstract
In this paper we present an algorithm for generating trajec- tories for a robot manipulator operating in an environment containing obstacles. The algorithm uses B-splines to generate trajectories in joint space with the constraint that the corresponding Cartesian space trajectories lie within specified tolerances. The latter is accomplished by modifying the joint space trajectory using an optimal search technique such that the constraints on the Cartesian space trajectory are satisfied. An important feature of the proposed algorithm is that an accurate joint space specification of the trajectory is available without performing numerous cornputationally expensive inverse kinematic transformations to convert a Cartesian space trajectory to a joint space trajectory. Further-more, by appropriately specifying the tolerance limits on the Cartesian space trajectory, we can ensure that collisions with obs tacles in Cartesian space are avoided. The use of the algorithm is PUMA 560 robot.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".