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arXiv · 2201.05475

SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems

Tingwei Meng·Zhen Zhang·Jérôme Darbon·George Em Karniadakis
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Abstract

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.

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Tingwei Meng, Zhen Zhang, Jérôme Darbon, George Em Karniadakis. 2022-01-14. SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems. https://arxiv.org/abs/2201.05475

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