{"ID":2831217,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.08630","arxiv_id":"2512.08630","title":"Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems","abstract":"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.","short_abstract":"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 Mul...","url_abs":"https://arxiv.org/abs/2512.08630","url_pdf":"https://arxiv.org/pdf/2512.08630v1","authors":"[\"Marta Manzoni\",\"Alessandro Nazzari\",\"Roberto Rubinacci\",\"Marco Lovera\"]","published":"2025-12-09T14:14:40Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.MA\"]","methods":"[]","has_code":false}