Human Robot Interaction for Hybrid Collision Avoidance System for Indoor Mobile Robots
Bibliographic record
Abstract
In this paper, a novel approach for collision avoidance for indoor mobile robots based on human-robot interaction is realized.The main contribution of this work is a new technique for collision avoidance by engaging the human and the robot in generating new collisionfree paths.In mobile robotics, collision avoidance is critical for the success of the robots in implementing their tasks, especially when the robots navigate in crowded and dynamic environments, which include humans.Traditional collision avoidance methods deal with the human as a dynamic obstacle, without taking into consideration that the human will also try to avoid the robot, and this causes the people and the robot to get confused, especially in crowded social places such as restaurants, hospitals, and laboratories.To avoid such scenarios, a reactive-supervised collision avoidance system for mobile robots based on human-robot interaction is implemented.In this method, both the robot and the human will collaborate in generating the collision avoidance via interaction.The person will notify the robot about the avoidance direction via interaction, and the robot will search for the optimal collision-free path on the selected direction.In case that no people interacted with the robot, it will select the navigation path autonomously and select the path that is closest to the goal location.The humans will interact with the robot using gesture recognition and Kinect sensor.To build the gesture recognition system, two models were used to classify these gestures, the first model is Back-Propagation Neural Network (BPNN), and the second model is Support Vector Machine (SVM).Furthermore, a novel collision avoidance system for avoiding the obstacles is implemented and integrated with the HRI system.The system is tested on H20 robot from DrRobot Company (Canada) and a set of experiments were implemented to report the performance of the system in interacting with the human and avoiding collisions.
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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 | 0.000 |
| 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".