论文标题
Sympocnet：解决最佳控制问题，并应用于高维多试路径计划问题
SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems
论文作者
论文摘要
在实时解决高维最佳控制问题是一个重要但具有挑战性的问题，鉴于近年来无人机的日益普及，因此在多代理路径计划问题上的应用引起了人们的注意。在本文中，我们提出了一种称为Sympocnet的新型神经网络方法，该方法应用了Symbletectic网络来解决具有状态约束的高维最佳控制问题。我们为二维和三维空间中的路径计划问题提供了几个数值结果。具体而言，我们证明了我们的精神网络可以在单个GPU上在1.5小时内解决500多个维度的问题，这显示了Sympocnet的有效性和效率。所提出的方法是可扩展的，并且有可能实时解决真正的高维路径计划问题。
Solving high-dimensional optimal control problems in real-time is an important but challenging problem, with applications to multi-agent path planning problems, which have drawn increased attention given the growing popularity of drones in recent years. In this paper, we propose a novel neural network method called SympOCnet that applies the Symplectic network to solve high-dimensional optimal control problems with state constraints. We present several numerical results on path planning problems in two-dimensional and three-dimensional spaces. Specifically, we demonstrate that our SympOCnet can solve a problem with more than 500 dimensions in 1.5 hours on a single GPU, which shows the effectiveness and efficiency of SympOCnet. The proposed method is scalable and has the potential to solve truly high-dimensional path planning problems in real-time.
