SADCHER: 異種ロボット群の動的連合を考慮したリアルタイムスケジューリング
SADCHER: Scheduling using Attention-based Dynamic Coalitions of Heterogeneous Robots in Real-Time
模倣学習とグラフアテンション・トランスフォーマーを用いて、異種マルチロボットチームの動的な連合形成とタスク優先順位制約を考慮したリアルタイムタスク割り当てを実現するフレームワークを提案。
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著者: Jakob Bichler, Andreu Matoses Gimenez, Javier Alonso-Mora
分類: cs.RO, cs.MA
原文アブストラクト
We present Sadcher, a real-time task assignment framework for heterogeneous multi-robot teams that incorporates dynamic coalition formation and task precedence constraints. Sadcher is trained through Imitation Learning and combines graph attention and transformers to predict assignment rewards between robots and tasks. Based on the predicted rewards, a relaxed bipartite matching step generates high-quality schedules with feasibility guarantees. We explicitly model robot and task positions, task durations, and robots' remaining processing times, enabling advanced temporal and spatial reasoning and generalization to environments with different spatiotemporal distributions compared to training. Trained on optimally solved small-scale instances, our method can scale to larger task sets and team sizes. Sadcher outperforms other learning-based and heuristic baselines on randomized, unseen problems for small and medium-sized teams with computation times suitable for real-time operation. We also explore sampling-based variants and evaluate scalability across robot and task counts. In addition, we release our dataset of 250,000 optimal schedules: https://autonomousrobots.nl/paper_websites/sadcher_MRTA/
