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.

翻译：近年来，空中机器人领域的进展使多旋翼飞行器能够用于自主货物运输。仅依赖经典方法对携带缆绳吊挂负载的四旋翼飞行器进行可靠建模面临显著挑战。另一方面，纯数据驱动的学习方法在设计中未遵循问题的物理约束，尤其在训练数据中状态分布稀疏的情形下。本文探索利用物理信息神经网络学习多旋翼-吊挂系统的端到端模型，并在给定时刻预测未来系统状态序列。采用带注意力机制的LSTM编码器-解码器来捕捉系统动力学特性。为保证系统多个预测状态间的一致性，我们在损失函数中引入基于物理的项，包含从第一性原理推导的离散化物理模型，以及允许预期值与预测值存在微小偏差的松弛变量。为训练模型，我们整理并公开了真实四旋翼携带吊挂负载的数据集。预测结果验证了该方法的可行性。所提出方法优于纯第一性原理物理模型以及未采用物理正则化的同类神经网络模型。