A Hybrid Two-layered Approach to Real-Time Motion Planning of Multiple Agents in Virtual Environments
Bibliographic record
Abstract
We proposed in a previous paper a hybrid technique, combining local steering behaviors and coordination graphs (CG), that allows real-time motion planning of multiple agents in a narrow passage. This hybrid technique not only avoids deadlocks, but also exhibits other interesting behaviors such as leader following, even though they are not explicitly coded in the algorithm. In this paper, we build upon the earlier result, and propose a two-layered approach to motion planning of multiple agents in virtual environments, consisting of open spaces connected by multiple narrow passages. The discrete generalized Voronoi diagram (GVD) of the static environment is used to identify all narrow passages automatically. The global path of each agent is also planned using the GVD. As each agent moves along its global path, it is locally modified using the hybrid technique combining steering behaviors with coordination graphs. Experimental results show that the resulting planner is able to plan motions of 30 agents in a virtual environment with three narrow passages in real-time, and the pre-processing phase of our approach is extremely fast. Since all planning is done in real-time, the approach allows an agent to change its final destination at runtime
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".