View Planning Problem with Combined View and Traveling Cost
Bibliographic record
Abstract
In this paper, we introduce the problem of view planning with combined view and traveling cost, denoted by traveling VPP. It refers to planning a sequence of sensing actions with minimum total cost by a robot-sensor system to completely inspect the surfaces of objects in a known workspace. The cost to minimize is a combination of the view cost, proportional to the number of viewpoints planned, and the traveling cost for the robot to realize them. First, we formulate traveling VPP as an integer linear program (ILP). The focus of this paper is to design an approximation algorithm that guarantees worst-case performance (w.r.t. the optimal solution cost). We propose a linear program based rounding algorithm that achieves an approximation ratio of the order of view frequency, defined to be the maximum number of viewpoints that see a single surface patch of the object. Together with the result we showed (2006), the best approximation ratio for Traveling VPP is either the order of view frequency or a poly-log function of the input size, whichever is smaller. Motivated from the robot motion planning techniques, where the graph built for robot traveling is a tree, we then consider the corresponding special case of traveling VPP, and give a polynomial sized LP formulation. We conclude with a discussion of realistic issues and constraints towards implementing our algorithm on real robot-sensor systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".