Optimal Motion Planning Under Dynamic Risk Region for Safe Human–Robot Cooperation
Bibliographic record
Abstract
As the development of factory automation, the workers and the robots are inevitable to collaborate in close proximity in a shared workspace, which makes the assurance of human safety a top priority. This article proposes a novel optimal motion planning framework for the manipulator to realize safe human–robot cooperation. To deal with the difficulties induced by the uncertainty and the sudden change of human movement, we design a novel dynamic risk region whose size is adjusted according to the predicted human velocity. Considering that the direct prediction of human velocity with low uncertainty is difficult due to the sensor noises and the errors involved with differential calculus, we first predict the human position at the next time step via Gaussian process regression, and then use it to predict the human velocity with the consideration of position prediction confidence. Then, we design the task controller by optimizing the performance index over an infinite time horizon, and design the safety-critical controller by extending the existing control barrier function-based method. Different from the existing works, we introduce a repulsive part to push the robot out when it enters the risk region, and provide an effective control gain design way to improve the adaptability in the dynamic environment. Finally, the simulation and experimental studies show that compared with the approaches with the fixed risk region and the simple proportional controller, our framework can get better trajectory tracking and safety performance in the dynamic environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".