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

TL;DR

The paper addresses the challenge of coordinating multiple UAVs for collision-free aerial 3D printing under task dependencies and resource constraints. It introduces a MILP-based safety-aware scheduling framework that integrates task assignment, precedence, timing, material and battery budgets, along with dynamic conflict handling and a unified objective that combines makespan, task importance, and agent utilization. Key contributions include task-importance prioritization, a one-shot optimization for dynamic team sizing, and formal guarantees of collision-free execution, validated through Gazebo simulations and multiple mesh geometries. The results show meaningful reductions in computation time and efficient fleet usage while maintaining safe operation, though the model adopts simplifying assumptions such as a linear battery-time relationship and fixed logistics times. Overall, the framework advances practical, scalable planning for multi-UAV aerial additive construction with explicit safety and efficiency considerations.

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.

Figures (10)

• Figure 1: Concept figure of the proposed framework where three robots $R_1-R_3$ are printing the original mesh according to the optimal scheduling plan generated by the algorithm. Their minimum clearance $r_c$ is visualized with a light blue sphere. The mission schedule is generated in a way that all potential collisions between UAVs are resolved and safety is maintained.
• Figure 2: Multi-Agent Aerial Additive Construction System Architecture Overview
• Figure 3: Rectangular mesh $M$ decomposed in $18$ chunks in a color-coded format (a) along with its dependency graph $G^d$ (b) and the Conflict Probability Graph $\mathcal{G}_c$ for pairs of tasks (c).
• Figure 4: Conflicting segment pairs for minimum clearance of $r_{c}$ m and their arrival times $t^m_i$ for each waypoint.
• Figure 5: Dependency graph along with annotated importance $\alpha_i$ for each node.