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.
@article{arxiv.2512.08630,
title = {Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems},
author = {Marta Manzoni and Alessandro Nazzari and Roberto Rubinacci and Marco Lovera},
journal= {arXiv preprint arXiv:2512.08630},
year = {2025}
}