Multi-UAV Cooperative and Continuous Path Planning for High-Resolution 3D Scene Reconstruction

Unmanned aerial vehicles (UAVs) are extensively employed for urban image captures and the reconstruction of large-scale 3D models due to their affordability and versatility. However, most commercial flight software lack support for the adaptive capture of multi-view images. Furthermore, the limited...

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Bibliographic Details
Published inDrones (Basel) Vol. 7; no. 9; p. 544
Main Authors Sui, Haigang, Zhang, Hao, Gou, Guohua, Wang, Xuanhao, Wang, Sheng, Li, Fei, Liu, Junyi
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2023
Subjects
Algorithms
Collaboration
continuous path planning
Control systems
cooperation
Cost allocation
Costs
Drone aircraft
Energy consumption
Energy costs
Heuristic
image capture
Image enhancement
Image reconstruction
Linear programming
Mathematical optimization
Methods
multi-UAV
Optimization
Path planning
Planning
scene reconstruction
Three dimensional models
Trajectory planning
Traveling salesman problem
Unmanned aerial vehicles
China
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Summary:Unmanned aerial vehicles (UAVs) are extensively employed for urban image captures and the reconstruction of large-scale 3D models due to their affordability and versatility. However, most commercial flight software lack support for the adaptive capture of multi-view images. Furthermore, the limited performance and battery capacity of a single UAV hinder efficient image capturing of large-scale scenes. To address these challenges, this paper presents a novel method for multi-UAV continuous trajectory planning aimed at the image captures and reconstructions of a scene. Our primary contribution lies in the development of a path planning framework rooted in task and search principles. Within this framework, we initially ascertain optimal task locations for capturing images by assessing scene reconstructability, thereby enhancing the overall quality of reconstructions. Furthermore, we curtail energy costs of trajectories by allocating task sequences, characterized by minimal corners and lengths, among multiple UAVs. Ultimately, we integrate considerations of energy costs, safety, and reconstructability into a unified optimization process, facilitating the search for optimal paths for multiple UAVs. Empirical evaluations demonstrate the efficacy of our approach in facilitating collaborative full-scene image captures by multiple UAVs, achieving low energy costs while attaining high-quality 3D reconstructions.
ISSN:2504-446X
2504-446X
DOI:10.3390/drones7090544