Online Data-Driven Fuzzy Clustering With Applications to Real-Time Robotic Tracking
Bibliographic record
Abstract
Robotic target tracking has been used in a variety of applications. Due to limited sampling rate, sensory characteristics and processing delays, an important issue in such systems is to extrapolate ahead the trajectory (position, orientation, velocity, and/or acceleration) of moving targets from past observations. This paper introduces a novel online data-driven fuzzy clustering algorithm that is based on the Maximum Entropy Principle for this particular task. In this algorithm, the fuzzy inference mechanism is extracted automatically from observed data without human help, which thus eliminates the necessity of expert's knowledge and a priori information on moving targets, as required by most traditional techniques. This algorithm does not require training, which enables it to work in a completely online fashion. Another important and distinct advantage of the algorithm exists in the fact that it is very fast and efficient in terms of computational cost and thus can be implemented in real time. In the meantime, the introduced algorithm is able to adapt quickly to the dynamics of moving targets. All these desired features make it especially suitable for the task to predict the trajectory of moving targets in robotic tracking. Simulation results show the effectiveness and efficiency of the presented algorithm.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".