Desensitized Trajectory Optimization for Hypersonic Vehicles

Makkapati, Venkata Ramana; Ridderhof, Jack; Tsiotras, Panagiotis; Hart, Joseph; Waanders, Bart van Bloemen

doi:10.1109/aero50100.2021.9438511

Cited by 9 publications

(1 citation statement)

References 23 publications

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“…Introduction. Optimal control methods are widely applied in many practical problems, which include path planning [17,83,43,25,75,59,65], humanoid robot control [30,34,56,36,33,26], and robot manipulator control [61,48,54,63,12]. With the increasing impact of drones in everyday tasks as well as specialized military tasks, the path planning problem of multiple drones has become increasingly important.…”

mentioning

confidence: 99%

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

Meng¹,

Zhang²,

Darbon³

et al. 2022

Preprint

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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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