Real-time collision avoidance for a redundant manipulator in an unstructured environment
Bibliographic record
Abstract
The problem of redundant manipulator collision avoidance in an unstructured environment is addressed in this paper based on the concept of modified impedance control. Instead of using a limited degree of redundancy (as in conventional methods) to find a collision-free trajectory, in the proposed approach, a robot's commanded joint torques are augmented by an "artificial" joint torque to provide correction for collision avoidance. "Artificial" collision forces are generated online according to the robot's posture and environment information (through knowledge of the robot kinematics and environment or proximity sensors on the robot). The corresponding artificial collision forces are converted to equivalent joint torques that would accomplish the collision avoidance manoeuvre. Then, the commanded joint torques are augmented so that a collision-free joint torque profile is achieved. Robot-to-environment collisions, robot self-collisions and robot constraints such as joint limit and singularity avoidance can be achieved using this method.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".