Optimal Safety-Aware Scheduling for Multi-Agent Aerial 3D Printing with Utility Maximization under Dependency Constraints

TL;DR

This paper introduces a comprehensive framework for safely coordinating multiple UAVs in 3D printing tasks, optimizing task scheduling, resource use, and UAV deployment to maximize utility while avoiding conflicts.

Contribution

It presents a novel optimization-based scheduling and coordination framework that accounts for task dependencies, safety constraints, and resource limitations in multi-UAV 3D printing.

Findings

Effective conflict-free scheduling demonstrated in simulation

Utility maximization balances UAV deployment and task completion

Framework adapts to structural and geometric task dependencies

Abstract

This article presents a novel coordination and task-planning framework to enable the simultaneous conflict-free collaboration of multiple unmanned aerial vehicles (UAVs) for aerial 3D printing. The proposed framework formulates an optimization problem that takes a construction mission divided into sub-tasks and a team of autonomous UAVs, along with limited volume and battery. It generates an optimal mission plan comprising task assignments and scheduling while accounting for task dependencies arising from the geometric and structural requirements of the 3D design, inter-UAV safety constraints, material usage, and total flight time of each UAV. The potential conflicts occurring during the simultaneous operation of the UAVs are addressed at a segment level by dynamically selecting the starting time and location of each task to guarantee collision-free parallel execution. An importance prioritization is proposed to accelerate the computation by guiding the solution toward more important tasks. Additionally, a utility maximization formulation is proposed to dynamically determine the optimal number of UAVs required for a given mission, balancing the trade-off between minimizing makespan and the deployment of excess agents. The proposed framework's effectiveness is evaluated through a Gazebo-based simulation setup, where agents are coordinated by a mission control module allocating the printing tasks based on the generated optimal scheduling plan while remaining within the material and battery constraints of each UAV.