用于多旋翼吊挂负载系统建模的物理信息神经网络
Physics-Informed Neural Network for Multirotor Slung Load Systems Modeling
AI总结：
针对多旋翼吊挂负载系统建模难题，提出融合物理约束的注意力LSTM编解码模型，通过含物理项的损失函数保证状态一致性，在真实数据集上表现优于纯物理模型和无物理正则的神经网络模型。
摘要：
空中机器人技术的最新进展使得多旋翼飞行器可用于自主有效载荷运输。仅依靠经典方法对搭载电缆吊挂负载的四旋翼飞行器进行可靠建模面临着巨大挑战。另一方面，纯数据驱动的学习方法在设计上并不符合该问题的物理约束，尤其是在训练数据中未被密集表征的状态下。在本研究中，我们探索利用物理信息神经网络来学习多旋翼-吊挂负载系统的端到端模型，并在给定时刻估计未来的系统状态序列。我们采用带注意力机制的LSTM编解码器来捕捉系统的动力学特性。为保证多个预测系统状态之间的一致性，我们提出在损失函数中加入一个基于物理的项，该项包含从第一性原理推导的离散化物理模型，以及允许期望值与预测值之间存在微小失配的松弛变量。为训练模型，我们整理了一个由真实四旋翼搭载吊挂负载采集的数据集，并将其公开。我们给出了预测结果，证实了该方法的可行性。所提出的方法性能优于第一性原理物理模型，也优于未采用本文提出的物理正则化训练的同类神经网络模型。
英文摘要：
Recent advances in aerial robotics have enabled the use of multirotor vehicles for autonomous payload transportation. Resorting only to classical methods to reliably model a quadrotor carrying a cable-slung load poses significant challenges. On the other hand, purely data-driven learning methods do not comply by design with the problem's physical constraints, especially in states that are not densely represented in training data. In this work, we explore the use of physics informed neural networks to learn an end-to-end model of the multirotor-slung-load system and, at a given time, estimate a sequence of the future system states. An LSTM encoder decoder with an attention mechanism is used to capture the dynamics of the system. To guarantee the cohesiveness between the multiple predicted states of the system, we propose the use of a physics-based term in the loss function, which includes a discretized physical model derived from first principles together with slack variables that allow for a small mismatch between expected and predicted values. To train the model, a dataset using a real-world quadrotor carrying a slung load was curated and is made available. Prediction results are presented and corroborate the feasibility of the approach. The proposed method outperforms both the first principles physical model and a comparable neural network model trained without the physics regularization proposed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
