An Extension of the Distance-Propagating Dynamic System for Robot Path Planning to Safe Obstacle Clearance
Bibliographic record
Abstract
In this paper we extend our previously presented efficient distance-propagating dynamic system for real-time robot path planning in dynamic environments to the case where safety margins around obstacles are included. Inclusion of safety margins approximately triples the number of arithmetic operations, however, the distance-propagating dynamic system is still very computationally efficient. The algorithm uses a grid representation of the environment, which need not be regular, and is applicable to dynamic environments where both targets and obstacles are permitted to move. No prior knowledge of target or obstacle movement is assumed. Safety margins around obstacles are implemented as "soft" margins defined by local penalty functions around obstacles which represent the extra distance the robot is willing to travel in order to avoid passing through this margin. The path through which the robot travels minimizes the sum of the current known distance to a target and the cumulative local penalty functions along the path. The effectiveness of the algorithm is demonstrated through a number of simulations.
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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 | 0.000 |
| 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 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".