Robot exploration in unknown cluttered environments when dealing with uncertainty
Bibliographic record
Abstract
The use of autonomous robots in urban search and rescue (USAR) missions has many potential benefits in terms of assisting rescue workers and increasing efficiency in these time- critical environments. However, the cluttered and unknown nature of these environments introduces uncertainty in both the sensing and actuation capabilities of a rescue robot. Such uncertainty has not been directly incorporated into the modeling of the USAR problem for existing robots. In this paper, we present the novel use of a partially observable Markov Decision Process (POMDP) method which directly incorporates uncertainty within the decision-making layer of the controller for a rescue robot. A hierarchical task structure is used to decompose the overall exploration and victim identification task of a robot into smaller subtasks. These subtasks are modeled as POMDPs taking into account sensory and actuation uncertainty. Our proposed approach was tested in numerous experiments in unknown and cluttered USAR-like environments. The results should that the approach was able to successfully explore the environments and find victims, while dealing with sensor and actuator uncertainty.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".