Conflict Resolution of Cluttered Multi-robot Systems Using Metaheuristic Optimization Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Conflict resolution becomes crucial when one is dealing with a large number of robots working in cluttered environments. The majority of the developed conflict resolution approaches in the literature deal with a motion-liveness problem which fails to gain collision-free movements for a large number of robots. This paper develops a systematic approach for coordinating the motions of multi-robot systems by adjusting their speeds to avoid collisions and guarantee motion-liveness of the robots. We mathematically formulate the multi-robot motion as a constrained optimization problem to minimize the time it takes for each robot to reach its target while avoiding collisions. Using two metaheuristic optimization methods, multiobjective genetic algorithm and particle swarm optimization, we can solve the conflict resolution problem up to 30 robots in a highly cluttered environment. Results show that we can find collision-free movements for a large number of robots in cluttered environments, while also guaranteeing multi-robot motion-liveness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it