Low Cost Autonomous Amphibious Bird Chasing Robot
Bibliographic record
Abstract
The use of artificial intelligence and machine learning to create autonomous robot platforms has been spreading into many applications recently, including animal behavior modification. Following this trend, we propose a low-cost, autonomous, and amphibious vehicle to modify the behavior of birds, such as Canadian geese, in commercial areas. Our robot patrols a predefined area set by GPS via an in-house developed Graphical User Interface (GUI). As it patrols this area along a predefined path, a Convolutional Neural Network (CNN) runs a goose detection algorithm to identify geese within a 5 m range. The robot also has basic collision avoidance through a combination of time-of-flight distance sensors and a bumper that detects physical collisions. Our solution is to chase the Canadian geese away from commercial areas frequented by humans such as golf courses before they nest and become territorial. This solution ensures that geese find safer and less disruptive nesting sites in a way that does not harm them. Moreover, the robot collects both locational and behavioral information by taking pictures, which provides information for bird behavior research. Our platform shows the potential to resolve human-animal contested environments with a low-cost intelligent robot solution that can be extended to many other applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".