用贝叶斯优化与STL逻辑协同规划多机器人路径，更省样本且更安全。
Multi-Robot Trajectory Planning via Constrained Bayesian Optimization and Local Cost Map Learning with STL-Based Conflict Resolution
- 单机用高斯过程学习局部代价图，少样本生成短路径
- 多机结合STL监控冲突，保证规范满足且可扩展
- 适合需要安全约束的无人船等实际场景
针对带有动力学约束和信号时序逻辑（STL）规范的多机器人运动规划问题，现有精确方法存在可扩展性瓶颈，传统采样方法需大量样本。本文提出两阶段框架：单机层面采用基于约束贝叶斯优化的树搜索（cBOT），利用高斯过程作为代理模型学习局部代价图与可行性约束，以更少样本生成更短无碰撞轨迹；多机层面设计增强型动力学冲突基础搜索算法（STL-KCBS），将STL监测融入冲突检测与解决，确保规范满足的同时保持可扩展性与概率完备性。基准测试显示轨迹效率与安全性优于现有方法。真实水面无人艇实验验证了在不确定环境中的鲁棒性与实用性。STLcBOT规划器将开源，实验视频见https://stlbot.github.io/。
原文摘要 · Abstract (English)
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/.
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