Sensor Planning for 3D Visual Search with Task Constraints
Bibliographic record
Abstract
Visual search is a fundamental problem in autonomous robotics. Traditionally, visual search is formulated as an optimization problem in which the sequence of actions ischosen based on immediate efficiency. In this paper we examine the effects of the task constraint in the form of maximum allowable cost on action selection in search. We propose three algorithms, namely Greedy Search with Constraint (GSC),Extended Greedy Search (EGS) and Dynamic Look Ahead Search (DLAS), to investigate which algorithm, whether locally or globally, has the most efficient performance under various conditions with a predefined task constraint. We examine our methods in environments of various sizes and configurations with three cost constraints including time, energy consumption and the distance travelled by the robot. Through extensive experiments on a mobile robot, we show that the environment characteristics as well as the type of constraint applied can alter the performance of the methods significantly. We also show that GSC algorithm, which relies on visual clues in an environment to optimize search, achieves the best and most efficient performance in comparison to EGS and DLAS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".