A hybrid-systems approach to potential field navigation for a multi-robot team
Why this work is in the frame
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Bibliographic record
Abstract
We consider potential field-based cooperative motion planning for a distributed team of semi-autonomous robots. We present a changing navigation function to allow the robots to incorporate new sensor data into their maps of the environment. We choose a Gaussian function to model attractors and a higher-order Gaussian-like function to model obstacles in order to avoid undesired local minima. Using arguments from hybrid systems theory, we show that this changing navigation function can be viewed as a mode-specific team Lyapunov function that stabilizes the system at all times. We. have verified our approach in simulations of a robot team mapping and foraging in an initially unknown environment. The team is able to map the environment, noting the location of all obstacles and attractive objects, then retrieve the attractors and return them to a goal position. Potential field navigation succeeds in this task while avoiding collisions between robots and obstacles as well as collisions among team members.
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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 | 0.001 | 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