多无人机协作搬运，无需通信也能实时自主规划。
Decentralized Real-Time Planning for Multi-UAV Cooperative Manipulation via Imitation Learning
- 用模仿学习让每架无人机独立学出高效运动策略。
- 实测性能接近中心化控制，训练仅需两小时。
- 适合无通信、强干扰的复杂环境，如灾后救援。
现有基于多无人机协同运输悬挂负载的方法通常依赖中心化控制架构或可靠的机间通信。本文提出一种基于机器学习的去中心化动力学规划方法，在部分可观测且无机间通信条件下仍可有效运行。该方法通过模仿学习，让每架无人机的去中心化学生策略模仿一个具备全局信息的中心化运动规划器。学生策略采用物理信息神经网络生成平滑轨迹，满足运动中的导数关系。训练时，学生策略利用教师策略生成的完整轨迹，提升样本效率。每个学生策略可在标准笔记本电脑上于两小时内完成训练。我们在仿真和真实环境中验证了该方法，使其能沿敏捷参考轨迹运动，性能与中心化方法相当。
原文摘要 · Abstract (English)
Existing approaches for transporting and manipulating cable-suspended loads using multiple UAVs along reference trajectories typically rely on either centralized control architectures or reliable inter-agent communication. In this work, we propose a novel machine learning based method for decentralized kinodynamic planning that operates effectively under partial observability and without inter-agent communication. Our method leverages imitation learning to train a decentralized student policy for each UAV by imitating a centralized kinodynamic motion planner with access to privileged global observations. The student policy generates smooth trajectories using physics-informed neural networks that respect the derivative relationships in motion. During training, the student policies utilize the full trajectory generated by the teacher policy, leading to improved sample efficiency. Moreover, each student policy can be trained in under two hours on a standard laptop. We validate our method in both simulation and real-world environments to follow an agile reference trajectory, demonstrating performance comparable to that of centralized approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。