What visual information is used for navigation around obstacles in a cluttered environment?
Bibliographic record
Abstract
The goal of this study was to determine what visual information is used to navigate around barriers in a cluttered terrain. Twelve traffic pylons were arranged randomly in a 4.55 x 3.15 m travel area: there were 20 different arrangements. For each arrangement, individuals (N = 6) were positioned in 1 of 3 locations on the outside border with their eyes closed: on verbal command they were instructed to open their eyes and quickly go to 1 of 2 specified goals (2 vertical posts defining a door) located on one edge of the travel area. The movement of the body was tracked using the OPTOTRAK system, with the IREDS placed on a collar worn by the subjects. Experimental data of travel path chosen were compared with those predicted by models that incorporated different types of visual information to control path trajectory. The 6 models basically use 2 different strategies for route selection: reactive control based on visual input about the obstacle encountered in the line-of-sight travel path (Model # 1) and path planning based on different visual information (Model # 2, 3, 4, 5, and 6). The models that involve path planning are grouped into 2 categories: models 2, 3, 4, and 5 need detailed geometrical configuration of the obstacles to plan a route while model 6 plans a route based on identifying and avoiding a cluster of obstacles in the travel path. Two measures were used to compare model performance with the actual travel path: the difference in area between predicted and actual travel path and the number of trials that accurately predicted the number of turns during travel. The results suggest that route selection is not based on reactive control, but does involve path planning. The model that best predicts the travel paths taken by the individuals uses visual information about cluster of obstacles and identification of safe corridors to plan a route.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".