Autonomous Navigation by Mobile Robots in Human Environments: A Survey
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
With the service robots are well used in more and more indoor environments, autonomous navigation in such a human environment has been explored in recent decades. Different from the traditional navigation schemes, the new scenarios pose challenges about how to deal with the dynamic obstacles, especially the humans. To overcome the challenges, researchers need to consider: 1) the uncertainty of humans motion, 2) the interaction between human and robot, 3) the group information of the people. Also, the energy cost in the navigation process is of vital importance. In this case, the navigation requirements go far from the shortest path. In this paper, we reviewed the related works in the past decade, which can be roughly divided into four categories: reactive based, predictive based, model based and learning based. For each category, we analyzed some state of the arts, and listed the pros, cons and open problems. In the last of the paper, we summarized some evaluation metrics and corresponding methods.
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.
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 | 0.001 | 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 | 0.001 | 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