A cooperative receding horizon controller for multi-target interception with Obstacle Avoidance
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
Most of the proposed methods in literature on multi-target interception and related problems such as pursuit-evasion still suffer from a major drawback: They do not account for the uncertainties inherited in the environment in many applications. In the authors' previous work, multi-target interception problem was investigated where uncertainties in the environment stem from the fact that targets were assumed to be moving objects with a priori unknown arrival times, positions and trajectories. In this paper, in addition to these uncertainties, the mission space is also assumed to contain obstacles. The problem is formulated as a reward collection mission, and subsequently, a cooperative receding horizon controller is utilized toward maximizing the total collected reward. Inspired by the urban areas, the cases with polygonal obstacles are discussed. The introduced scheme is then adapted to improve the computational efficacy of algorithm. Analytical aspects of problem are discussed. The effectiveness and advantages of the proposed algorithm are demonstrated via numerical simulations.
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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