Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems

• URL: http://arxiv.org/abs/2512.08630v1
• Date: Tue, 09 Dec 2025 14:14:40 GMT
• Title: Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems
• Abstract summary: We treat each task as a trajectory generation scenario defined by a specific number of drone-to-drone interactions.<n>We employ Multi-Task Gaussian Processes, which capture shared structure across tasks and enable efficient information transfer.<n>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.
• Score: 39.146761527401424
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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