多无人机协同3D打印,兼顾安全、依赖与效率最优。
Optimal Safety-Aware Scheduling for Multi-Agent Aerial 3D Printing with Utility Maximization under Dependency Constraints
- 动态调度+任务优先级,实现多机无冲突并行打印。
- 在材料与电量约束下,优化任务分配与飞行时间。
- 适合需要高效多机协同的智能建造场景。
本文提出一种新型协调与任务规划框架,支持多架无人飞行器(UAV)在空中3D打印任务中无冲突地同步协作。该框架将施工任务分解为子任务,结合自主无人机团队及有限载荷与电池容量,构建优化问题,生成包含任务分配与调度的最优计划。考虑3D设计的几何与结构依赖、无人机间安全距离、材料使用量及每架无人机总飞行时间等约束。通过动态选择各任务的起始时间与位置,在段级解决多机同时作业时的潜在冲突,确保并行执行无碰撞。引入重要性优先策略加速求解,引导算法聚焦关键任务。此外,提出效用最大化模型,动态确定完成任务所需的最优无人机数量,在最小化完工时间与避免冗余部署之间取得平衡。框架有效性通过基于Gazebo的仿真验证,任务由任务控制模块依据生成的最优调度计划分配,所有无人机均在材料与电池约束范围内运行。
原文摘要 · Abstract (English)
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.
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