Although deep learning techniques have been successfully implemented to solve domain-specific unmanned aerial vehicle planning problems, it is still a challenging task to develop a learning method to solve multidomain planning problems. Because the multidomain problems often involve learning more parameters, a dilated dataset diminishes learning speed due to its size and high dimensionality. The following two observations help tackle the issue: the state space of planning problems can be decomposed into representations of the domain state and system state, and the dimensionality problem often arises due to the huge size of the domain rather than system state. Inspired by such observations, this work presents a learning framework consisting of two networks: 1) a domain abstraction network in the form of a variational autoencoder that reduces the dimension of the domain space into a compact form, and 2) a planning network that generates a planning solution for a given domain setting. The effectiveness of the proposed learning framework is validated in case studies.

尽管深度学习技术已成功应用于解决特定领域的无人机规划问题，但开发一种解决多领域规划问题的学习方法仍然是一项具有挑战性的任务。由于多域问题通常涉及学习更多参数，因此扩张的数据集会因其大小和高维数而降低学习速度。以下两个观察有助于解决这个问题：规划问题的状态空间可以分解为域状态和系统状态的表示，维数问题通常由于域的巨大规模而不是系统状态而出现。受这些观察的启发，这项工作提出了一个由两个网络组成的学习框架：1) 变分自编码器形式的域抽象网络，将域空间的维度降低为紧凑形式，以及 2) 规划网络，为给定的域设置生成规划解决方案。所提出的学习框架的有效性在案例研究中得到了验证。