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

2025-12-05

AI Summary

This work addresses the safe scheduling optimization problem for collaborative aerial 3D printing by multiple UAVs, subject to concurrent constraints including task precedence, inter-UAV minimum safety distance, material consumption, battery endurance, and payload capacity. We propose a dependency-graph-based combinatorial optimization model and design a dynamic scheduling algorithm incorporating an importance-priority heuristic to accelerate convergence. Furthermore, we formulate a utility-maximization framework that adaptively determines the optimal number of UAVs, achieving a Pareto-optimal trade-off between makespan reduction and resource expenditure. Extensive Gazebo-based simulations demonstrate that the proposed approach generates large-scale, collision-free, parallel printing schedules satisfying all hard constraints, thereby significantly enhancing system safety and overall operational efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Optimizes multi-UAV task scheduling for aerial 3D printing
• Ensures collision-free parallel execution under safety and dependency constraints
• Maximizes utility by balancing makespan and UAV deployment trade-offs

Innovation

• Multi-UAV coordination for aerial 3D printing
• Dynamic scheduling to prevent collisions and manage dependencies
• Utility maximization to optimize UAV count and mission time