A hybrid collision avoidance system for indoor mobile robots based on human-robot interaction
Bibliographic record
Abstract
This paper presents a novel approach for collision avoidance for indoor mobile robots based on human-robot interaction. The main contribution of this work is a new technique for collision avoidance by engaging the human and the robot in generating new collision-free paths. In mobile robotics, collision avoidance is critical for the success of the robots in implementing their tasks, especially when the robots work in cluttered and dynamic environments, which include humans. Traditional obstacle avoidance methods deal with the human as dynamic obstacles, without taking into consideration that the human will also try to avoid the robot, and this might cause a collision when both take the same avoidance path. To evade such situations, a supervised collision avoidance system for indoor mobile robots based on 3D vision and human-robot interaction is proposed. In this method, both the robot and the human will collaborate in generating the collision avoidance via interaction. The robot will notify the human about its existence via voice messages. After a certain distance, the robot will ask the human to interact. If a user interacted with the robot, it will execute the collision-avoidance path based on the interaction; else the robot will calculate the collision-free path autonomously. Kinect sensor is used for human detection, and two methods are compared which are Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) for gesture recognition. Furthermore, a robust collision avoidance system is implemented which is fused with the implemented HRI system to avoid collisions with humans. The system is tested on H20 robot (DrRobot Company, Canada) and the experiments proved the strength of the proposed method in interacting with the human and avoiding collisions with 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".