Voxel-Based Path Planning for Autonomous Vehicles in Parking Lots

With the development of autonomous driving technology, the application scenarios for mobile robots and unmanned vehicles are gradually expanding from simple structured environments to complex unstructured scenes. In unstructured three-dimensional spaces such as urban environments, traditional two-dimensional map construction and path planning techniques struggle to effectively plan accurate paths. To address this, this paper proposes a method of constructing a map and planning a route based on three-dimensional spatial representation. This method utilizes point cloud semantic segmentation to extract navigable space information from environmental point cloud data and employs voxelization techniques to generate a voxel map. Building on this, the paper combines a variable search neighborhood A* algorithm with a road-edge-detection-based path adjustment to generate optimal paths between two points on the map, ensuring that the paths are both short and capable of effectively avoiding obstacles. Our experimental results in multi-story parking garages show that the proposed method effectively avoids narrow areas that are difficult for vehicles to pass through, increasing the average edge distance of the path by 83.3% and significantly enhancing path safety and feasibility.