A Simulated Annealing Algorithm and Grid Map-Based UAV Coverage Path Planning Method for 3D Reconstruction

Sichen Xiao,Xiaojun Tan,Jinping Wang

doi:10.3390/electronics10070853

Abstract

With the extensive application of 3D maps, acquiring high-quality images with unmanned aerial vehicles (UAVs) for precise 3D reconstruction has become a prominent topic of study. In this research, we proposed a coverage path planning method for UAVs to achieve full coverage of a target area and to collect high-resolution images while considering the overlap ratio of the collected images and energy consumption of clustered UAVs. The overlap ratio of the collected image set is guaranteed through a map decomposition method, which can ensure that the reconstruction results will not get affected by model breaking. In consideration of the small battery capacity of common commercial quadrotor UAVs, ray-scan-based area division was adopted to segment the target area, and near-optimized paths in subareas were calculated by a simulated annealing algorithm to find near-optimized paths, which can achieve balanced task assignment for UAV formations and minimum energy consumption for each UAV. The proposed system was validated through a site experiment and achieved a reduction in path length of approximately 12.6% compared to the traditional zigzag path.

Highlights

With the development of electronic technology and modern control technology, unmanned aerial vehicles (UAVs) are being increasingly applied in civilian affairs in addition to their previous use in the military field [1]
Unlike large UAVs that can individually execute a surveillance or strike mission, small-scale UAVs operate as a system and are often used in formations, which can decrease time consumption and improve robustness [2]
One important application of UAVs is for 3D reconstruction: UAVs collect images from a specified target area, and the 3D model of the area will be reconstructed from those images on a computer

Summary

Introduction

With the development of electronic technology and modern control technology, unmanned aerial vehicles (UAVs) are being increasingly applied in civilian affairs in addition to their previous use in the military field [1]. Unlike large UAVs that can individually execute a surveillance or strike mission, small-scale UAVs operate as a system and are often used in formations, which can decrease time consumption and improve robustness [2]. Such vehicles are used in many fields, including environmental and natural disaster monitoring, border surveillance, emergency assistance, search and rescue missions, delivery of goods, and construction [3]. The result of the reconstructed 3D model is highly dependent on the quality of the image resources. The dataset should meet various requirements related to geometry discrimination, texture variance, sample sufficiency, content overlap, and image resolution [7]

Objectives

Methods

Results