Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908701857677312 |
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| author | Manzoni, Marta Nazzari, Alessandro Rubinacci, Roberto Lovera, Marco |
| author_facet | Manzoni, Marta Nazzari, Alessandro Rubinacci, Roberto Lovera, Marco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems Manzoni, Marta Nazzari, Alessandro Rubinacci, Roberto Lovera, Marco Robotics Multiagent Systems 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. |
| title | Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2512.08630 |