Three-Dimensional Path Planning: Navigating through Rough Mereology

TL;DR

This paper introduces a novel 3D path planning algorithm for flying robots using Rough Mereology, combining potential fields and real-time environment mapping to improve obstacle avoidance for drone navigation.

Contribution

It develops a new 3D path planning method based on mereological potential fields and real-time environment recognition for drone navigation.

Findings

Successfully generated paths avoiding obstacles in 3D environments

Integrated real-time environment mapping with path planning

Enhanced drone navigation capabilities in complex environments

Abstract

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.