In this paper, we present an innovative technique for the path planning of flying robots in a 3D environment in Rough Mereology terms. The main goal was to construct the algorithm that would generate the mereological potential fields in 3-dimensional space. To avoid falling into the local minimum, we assist with a weighted Euclidean distance. Moreover, a searching path from the start point to the target, with respect to avoiding the obstacles was applied. The environment was created by connecting two cameras working in real-time. To determine the gate and elements of the world inside the map was responsible the Python Library OpenCV [1] which recognized shapes and colors. The main purpose of this paper is to apply the given results to drones.

翻译：本文提出了一种在粗糙分体论框架下对三维环境中飞行机器人进行路径规划的创新技术。主要目标是构建能够在三维空间中生成分体论势场的算法。为避免陷入局部极小值，我们引入加权欧氏距离作为辅助手段。此外，还采用了从起点到目标点的搜索路径策略，同时规避障碍物。环境通过连接两个实时工作的摄像头构建而成。地图内的门闩及世界元素识别由Python库OpenCV[1]负责实现，该库可识别形状与颜色。本文的主要目的是将上述研究成果应用于无人机。