Safe Motion Planning for Human-Robot Interaction: Design and Experiments
Bibliographic record
Abstract
The proposed safe planner reduces the factors affecting danger along the path. Using the two-stage planning approach reduces the depth of local minima in the cost function, allowing the planner to execute quickly. Minimizing the danger criterion during the planning stage ensures that the robot is in a low inertia configuration in the case of an unanticipated collision, as well as reducing the chance of a collision by distancing the robot centre of mass from the user. This advance-planning approach puts the robot in a better position to deal with real-time safety hazards. When an inverse kinematics routine is available for an articulated robot, the performance of the planner can be further improved by adding a backwards search. That is, the path is generated backwards from the goal when the goal location is in an area crowded by obstacles. To ensure that the forwards and backwards generated paths meet, a posture potential is added to the total cost function. By including the posture potential directly into the cost function, rather than splining the two paths after they are generated, the algorithm ensures that posture correction occurs during low-danger sections of the path. The proposed method was verified in simulation and experiments. A user study was also carried out to determine if the safe planned motion affected the perception of safety by users. Two types of robot motions were presented to human subjects during the study: motions planned with a conventional potential field planner, and motions planned with the safe planner. Subjects reported significantly less anxiety and surprise, and reported feeling more calm when safe planned motions were presented, as compared to the conventional potential field planner.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".