A Dataset of Orthogonal Polygon-Derived Maze Environments for Path Planning Benchmarking

Version 1.2: This version includes updated benchmark results and corrected dataset files. This is the recommended version for use. The dataset is organized into a structured directory format to facilitate access and reuse. It consists of orthogonal polygon data, corresponding grid-based maze representations, visualization images, and metadata files. The primary purpose of this dataset is to provide a systematic collection of orthogonal polygon-derived maze environments for benchmarking path planning algorithms. By covering a wide range of geometric complexities and providing standardized start–goal configurations, the dataset supports consistent and reproducible evaluation across different algorithmic approaches. Beyond benchmarking, the dataset can also be used in related application contexts. The grid-based maze representations are suitable for developing and testing navigation algorithms in artificial intelligence and robotics. In addition, the dataset can be applied in game development, particularly for procedural maze generation and navigation scenarios. Furthermore, the visualization results in the images/path_visuals directory provide insight into the behavior of different path planning algorithms, including explored regions and final trajectories. These visual outputs may serve as data for training or evaluating learning-based approaches that aim to model or approximate path planning strategies. The dataset covers a wide range of geometric complexities by considering 20 different vertex counts, ranging from 50 to 100,000 vertices. For each vertex count, 50 polygon instances are generated, resulting in a total of 1,000 orthogonal polygons and 1,000 corresponding maze instances. The polygons directory contains orthogonal polygon instances grouped by the number of vertices. Each file stores a single polygon in CSV format, where each row corresponds to a vertex represented by its Cartesian coordinates (x, y). The vertices are ordered along the boundary of the polygon, forming a simple orthogonal shape without self-intersections. The mazes directory contains grid-based representations derived from the corresponding polygon instances. Each maze is stored in JSON format and includes the grid size, a binary occupancy grid, and start–goal configurations. In the grid representation, cells are encoded as binary values, where 0 denotes free space and 1 denotes obstacles. The polygon boundary and exterior regions are treated as obstacles, while the interior defines the navigable space. The start and goal positions are generated using a two-sweep Breadth-First Search procedure, which approximates a long shortest path within the maze. The images/maze directory provides visualizations of the grid-based maze instances. This directory contains 1,000 images, each corresponding to a maze environment with annotated start and goal positions. The images/path_visuals directory contains visualization results of path planning algorithms applied to the maze instances. For each maze, six algorithms are evaluated, including BFS, Dijkstra, A*, PRM, RRT-Connect, and RRT*. As a result, this directory contains 6,000 images illustrating the computed paths and, where applicable, intermediate exploration structures. The metadata directory contains summary files that enable indexing and basic analysis. The file dataset_summary.csv provides an overview of all instances, including identifiers, number of vertices, file references, and start–goal configurations. The file pathfinding_summary.csv records benchmarking results for all algorithm–maze pairs, including success status, path length, and execution time. Overall, the dataset comprises 1,000 polygon files (CSV), 1,000 maze files (JSON), 1,000 maze visualization images, and 6,000 path planning result images, providing a comprehensive resource for evaluating path planning algorithms across environments of varying complexity. All data files are organized to allow direct use in computational experiments without additional preprocessing. This dataset is associated with the data article:"A Dataset of Orthogonal Polygon-Derived Maze Environments for Path Planning Benchmarking" by Nguyen Kieu Linh.版本1.2：本版本更新了基准测试结果并修正了数据集文件，为官方推荐使用版本。 本数据集采用结构化目录格式组织，便于访问与复用，包含正交多边形（orthogonal polygon）数据、对应的基于网格的迷宫表示、可视化图像与元数据文件。 本数据集的核心用途是提供一套系统化的、由正交多边形衍生的迷宫环境集合，用于路径规划（path planning）算法的基准测试。该数据集覆盖了广泛的几何复杂度范围，并提供标准化的起点-终点配置，支持对不同算法方案开展一致性、可复现的评估。 除基准测试外，本数据集还可应用于相关研究场景：基于网格的迷宫表示可用于开发与测试人工智能（artificial intelligence, AI）及机器人学领域的导航算法；此外，该数据集还可用于游戏开发，尤其适用于程序化迷宫生成与导航相关的应用场景。 此外，images/path_visuals 目录下的可视化结果可用于解析不同路径规划算法的运行行为，包括算法探索的区域与最终轨迹。这些可视化输出可作为训练或评估基于学习的方法的数据集，用于建模或近似路径规划策略。 本数据集通过涵盖20种不同的顶点数量（范围为50至100,000个顶点）来覆盖多样的几何复杂度。针对每种顶点数量，均生成50个多边形实例，最终总计包含1000个正交多边形与1000个对应的迷宫实例。 polygons 目录下的正交多边形实例按顶点数量分组存储。每个文件以CSV格式存储单个多边形，每一行对应一个顶点，以笛卡尔直角坐标系(x, y)坐标表示。顶点沿多边形边界有序排列，构成无自交的简单正交多边形。 mazes 目录下存储了从对应多边形实例衍生的基于网格的表示。每个迷宫以JSON格式存储，包含网格尺寸、二进制占用网格与起点-终点配置。在网格表示中，单元格以二进制值编码：0代表可通行区域，1代表障碍物。多边形边界与外部区域均被视为障碍物，而多边形内部则定义为可导航空间。起点与终点通过两次扫描的广度优先搜索（Breadth-First Search, BFS）流程生成，该流程可近似得到迷宫中较长的最短路径。 images/maze 目录提供了基于网格的迷宫实例的可视化效果，该目录包含1000张图像，每张对应一个标注了起点与终点位置的迷宫环境。 images/path_visuals 目录存储了针对迷宫实例运行路径规划算法得到的可视化结果。针对每个迷宫，共评估了6种算法，包括BFS、Dijkstra、A*、PRM、RRT-Connect与RRT*。因此该目录包含6000张图像，用于展示计算得到的路径以及（适用时的）中间探索结构。 metadata 目录包含用于索引与基础分析的汇总文件：dataset_summary.csv 文件提供了所有实例的概览，包括标识符、顶点数量、文件引用与起点-终点配置；pathfinding_summary.csv 文件则记录了所有算法-迷宫组合的基准测试结果，包括运行状态、路径长度与执行耗时。 总体而言，本数据集包含1000个CSV格式的多边形文件、1000个JSON格式的迷宫文件、1000张迷宫可视化图像与6000张路径规划结果图像，为不同复杂度环境下的路径规划算法评估提供了一套全面的资源。 所有数据文件均经过合理组织，可直接用于计算实验，无需额外预处理。 本数据集关联的学术论文为：Nguyen Kieu Linh 撰写的《用于路径规划基准测试的正交多边形衍生迷宫环境数据集》（"A Dataset of Orthogonal Polygon-Derived Maze Environments for Path Planning Benchmarking"）。