On Safe Robot Navigation Among Humans as Dynamic Obstacles in Unknown Indoor Environments
Bibliographic record
Abstract
In this paper, we rigorously test two conjectures in mobile robot navigation among dynamic obstacles in unknown environments: i) a planner for static obstacles, if executed at a fast update rate (i.e., fast replanning), might be quite effective in dealing with dynamic obstacles, and ii) existing implemented planners have been effective in humans environments (with humans being dynamic obstacles) primarily because humans themselves avoid the robot and if this were not the case, robot will run into collisions with humans much more frequently. The core planning approach used is a Global path planner combined with a local Dynamic Window planner with repeated re-planning (GDW). We compare two planners within this framework: i) all obstacles are treated as static (GDW-S) and ii) predicted trajectories of dynamic obstacles are used to avoid future collisions within a given planning horizon time (GDW-D). The effect of humans avoiding robot (and other humans) is simulated via a simple local potential field based approach. We indicate such environments by a suffix +R (repulsion) for the corresponding planner. Hence there are four categories that we tested: GDW-S, GDW-D, GDW-S+R and GDW-D+R in different environments of varying complexity. The performance metrics used were the percentage of successful runs without collisions and total number of collisions. The results indicate that i) GDW-D planner outperforms GDW-S planner, i.e., conjecture 1 is false, and ii) humans avoiding robots does result in more successful runs, i.e., conjecture ii) is true. Furthermore, we've implemented both GDW-S and GDW-D planners on a real system and report experimental results for single obstacle case.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".