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

TL;DR

This paper introduces a Multi-Task Bayesian Optimization framework to efficiently tune decentralized trajectory algorithms for multi-UAV systems, leveraging shared information across scenarios to improve optimization efficiency.

Contribution

It presents a novel application of Multi-Task Gaussian Processes for tuning multi-UAV trajectory generation, comparing single-task and average-task optimization strategies.

Findings

Single-task optimization reduces mission time as swarm size increases.

Average-task optimization is more time-efficient than single-task.

Shared modeling across tasks improves tuning efficiency.

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.