制約付きベイズ最適化と局所コストマップ学習によるマルチロボット軌道計画：STLに基づく衝突解決を統合して
Multi-Robot Trajectory Planning via Constrained Bayesian Optimization and Local Cost Map Learning with STL-Based Conflict Resolution
信号時相論理（STL）仕様と運動学的制約下でのマルチロボット軌道計画を、単一ロボット用の制約付きベイズ最適化木探索と、STL監視を組み込んだ衝突ベース探索を統合した2段階フレームワークで解決する。
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著者: Sourav Raxit, Abdullah Al Redwan Newaz, Jose Fuentes, Paulo Padrao, Ana Cavalcanti, Leonardo Bobadilla
分類: cs.RO
原文アブストラクト
We address multi-robot motion planning under Signal Temporal Logic (STL) specifications with kinodynamic constraints. Exact approaches face scalability bottlenecks and limited adaptability, while conventional sampling-based methods require excessive samples to construct optimal trajectories. We propose a two-stage framework integrating sampling-based online learning with formal STL reasoning. At the single-robot level, our constrained Bayesian Optimization-based Tree search (cBOT) planner uses a Gaussian process as a surrogate model to learn local cost maps and feasibility constraints, generating shorter collision-free trajectories with fewer samples. At the multi-robot level, our STL-enhanced Kinodynamic Conflict-Based Search (STL-KCBS) algorithm incorporates STL monitoring into conflict detection and resolution, ensuring specification satisfaction while maintaining scalability and probabilistic completeness. Benchmarking demonstrates improved trajectory efficiency and safety over existing methods. Real-world experiments with autonomous surface vehicles validate robustness and practical applicability in uncertain environments. The STLcBOT Planner will be released as an open-source package, and videos of real-world and simulated experiments are available at https://stlbot.github.io/.
