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Euler-Lagrange as Pseudo-metric of the RRT algorithm for optimal-time trajectory of flight simulation model in high-density obstacle environment

机译:高密度障碍环境下飞行仿真模型最优时间轨迹的RRT算法的伪度量Euler-Lagrange

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This paper introduces a solution to the problem of steering an aerodynamical system, with non-holonomic constraints superimposed on dynamic equations of motion. The proposed approach is a dimensionality reduction of the Optimal Control Problem (OCP) with heavy path constraints to be solved by Rapidly-Exploring Random Tree (RRT) algorithm. In this research, we formulated and solved the OCP with Euler-Lagrange formula in order to find the optimal-time trajectory. The RRT constructs a non-collision path in static, high-dense obstacle environment (i.e. heavy path constraint). Based on a real-world aircraft model, our simulation results found the collision-free path and gave improvements of time and fuel consumption of the optimized Hamiltonian-based model over the original non-optimized model.
机译:本文介绍了一种解决空气动力学系统转向问题的方法,将非完整约束叠加在动态运动方程上。所提出的方法是通过快速探索随机树(RRT)算法解决具有较大路径约束的最优控制问题(OCP)的降维。在这项研究中,我们用Euler-Lagrange公式制定并求解了OCP,以找到最佳时间轨迹。 RRT在静态,高密度障碍环境(即重路径约束)中构造了非冲突路径。基于真实世界的飞机模型,我们的仿真结果找到了无碰撞的路径,并比原始的非优化模型改善了基于汉密尔顿优化模型的时间和燃油消耗。

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