计算机科学
数学优化
离散化
障碍物
路径(计算)
约束(计算机辅助设计)
A*搜索算法
算法
过程(计算)
启发式
随机树
采样(信号处理)
集合(抽象数据类型)
明星(博弈论)
树(集合论)
运动规划
数学
人工智能
数学分析
几何学
滤波器(信号处理)
政治学
机器人
法学
计算机视觉
程序设计语言
操作系统
作者
Hao Pu,Xinjie Wan,Taoran Song,Paul Schonfeld,Lihui Peng
标识
DOI:10.1016/j.engappai.2023.107770
摘要
Railway alignment development in mountainous regions is a complex problem, especially when there are numerous obstacles in the study area. Obtaining a feasible solution that satisfies all the obstacle constraints requires considerable computing resources and time. To solve this problem, a graph-based shortest path method, i.e., three-dimensional rapidly exploring random tree star (3D-RRT-star), is customized. Three main innovations are included in this method: (1) A heuristic sampling process is proposed to avoid getting the RRT search trapped into local ranges and overlooking possible path alternatives by combining a specially designed RRT node sampling method and a railway spatially-reachable analysis. (2) A multi-level constraint discretization approach is proposed to describe the obstacles in the study area, while procedures are developed to tackle the obstacle constraints dynamically during the search process. (3) An evolutionary search method integrating a sampling strategy and constraint handling operator is devised for generating a set of dissimilar RRT paths, which are finally refined into railway alignments satisfying curve constraints. Ultimately, the proposed method is applied to a realistic railway case. The experimental results confirm that it can yield a better alignment than the best manually obtained solution. Furthermore, its search efficiency is compared to a previous optimization method for this problem and the results reveal significant improvement.
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