BV（Braitenberg Vehicles）-NEAT（NeuroEvolution of Augmenting Topologies）

The dataset is designed to support research on path planning in complex dynamic environments, where challenges such as inefficient search, high computational cost, limited path smoothness, and insufficient obstacle avoidance commonly exist. It consists of simulated dynamic scenarios that include varying obstacle distributions, dynamic obstacle movements, and complex environmental configurations to comprehensively evaluate global and local path-planning performance.The dataset integrates environments suitable for both global planning and local adaptive navigation. Global path-planning data are constructed to evaluate the performance of an improved A* algorithm in terms of search efficiency and computational cost, while dynamic navigation data are designed to assess adaptive obstacle avoidance and real-time response capabilities using a BV (Braitenberg Vehicles)–NEAT (NeuroEvolution of Augmenting Topologies) framework.