用多任务贝叶斯优化，高效调优多无人机编队轨迹生成。
Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems
- 基于多任务高斯过程建模不同交互场景间的共享结构。
- 单任务优化虽快但随机群规模增大任务时间缩短更明显。
- 适合需要快速部署且对全局性能有要求的无人机协同系统。
本文研究在多无人机系统中使用多任务贝叶斯优化来调优去中心化轨迹生成算法。我们将每个任务视为由特定数量的无人机间交互定义的轨迹生成场景。为建模跨场景关系，采用多任务高斯过程，捕捉任务间的共享结构，并在优化过程中实现高效信息传递。我们对比两种策略：在所有任务上优化平均任务时间，以及单独优化每个任务。通过全面的仿真测试表明，单任务优化随着群体规模增大，任务时间逐渐缩短，但所需优化时间显著长于平均任务方法。
原文摘要 · Abstract (English)
This paper investigates the use of Multi-Task Bayesian Optimization for tuning decentralized trajectory generation algorithms in multi-drone systems. We treat each task as a trajectory generation scenario defined by a specific number of drone-to-drone interactions. To model relationships across scenarios, we employ Multi-Task Gaussian Processes, which capture shared structure across tasks and enable efficient information transfer during optimization. We compare two strategies: optimizing the average mission time across all tasks and optimizing each task individually. Through a comprehensive simulation campaign, we show that single-task optimization leads to progressively shorter mission times as swarm size grows, but requires significantly more optimization time than the average-task approach.
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