Area-covering operation of a cleaning robot in a dynamic environment with unforeseen obstacles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Area-covering operation is a special kind of path planning, which requires the robot path to cover every part of the workspace. Area covering is an essential issue for cleaning robots and many other robotic applications such as painter robots, land mine detectors, lawn mowers, and windows cleaners. In this paper, a novel biologically inspired neural network approach to area-covering operation with avoidance of unforeseen obstacles is proposed for a cleaning robot in a dynamic environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from a biological membrane model. There are only local lateral connections among neurons, thus the computational complexity depends linearly on the neural network size. The proposed approach is compared to fuzzy logic based, rule based and re-planning based models. It shows that the proposed model is capable of planning more reasonable and shorter area-covering path with obstacle avoidance. The proposed model algorithm is computationally efficient, and can also deal with changing environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it