Interactive collision avoidance system for indoor mobile robots based on human-robot interaction
Bibliographic record
Abstract
This paper presents a collision avoidance system based on human-robot interaction for mobile robots, which are working alongside to humans. In the future work environments, mobile robots will work side by side to humans. This raises big challenges related to the safety of the human and the ability of the robot to identify the people, interact with them and avoid physical accidents to them. To realize these concepts, a human-robot interaction system together with a collision avoidance system are developed to enable a safe navigation of the mobile robots. In this system, when a robot meets people in its path, it will try to interact with the human to execute one of the three actions: move forward, move backward, and collision avoidance; or the robot will implement the collision-avoidance autonomously when no people interacted with it. The interaction is based on the gestures obtained from the Kinect 2.0 sensor, and the system was tested using a H20 Robot (Canada). The experimental results proved the validity of the proposed system in interacting with the humans, and avoiding them.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".