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.

翻译：本文研究了多任务贝叶斯优化在多无人机系统中用于调优去中心化轨迹生成算法的应用。我们将每个任务视为由特定数量的无人机间交互所定义的轨迹生成场景。为建模不同场景间的关系，我们采用多任务高斯过程，该方法能捕捉任务间的共享结构，并在优化过程中实现高效的信息传递。我们比较了两种策略：优化所有任务的平均任务时间，以及单独优化每个任务。通过全面的仿真实验，我们发现单任务优化能随集群规模增大而逐步缩短任务时间，但其所需的优化时间显著高于平均任务优化方法。