Asymptotically Optimal Solutions for 3D Reach-Avoid Games with Exclusion Zones Using Informed-Expansive-Spaces-Tree
Bibliographic record
Abstract
This paper addresses the reach-avoid game in 3D, where a pursuer attempts to capture an evader while avoiding danger zones, and the evader seeks to reach a target without being captured. Traditional control-based methods struggle with complex scenarios or lack the solution’s guarantees. This paper casts the reach-avoid problem as a planning task, enabling probabilistic complete and asymptotically optimal open-loop solutions assuming the worst-case of the evader in more complex scenarios (i.e., optimal control). To solve this planning problem, this paper proposes the Informed-Expansive-Spaces-Tree (Informed-EST) to restrict the search space after an initial path is found, reducing computational overhead compared with other sampling-based planning algorithms while maintaining the asymptotically optimal guarantees. Evaluations against Rapidly-exploring random tree (RRT), EST and RRT*in reach-avoid scenarios with static and moving exclusion zones highlight improvements in computational efficiency and performance. Additionally, critical limitations of RRT*in reach-avoid scenarios are identified and discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".