A Novel Spherical Shortest Path Planning Method for UAVs
As a central subdivision of the low-altitude economy industry, industrial and consumer drones have broad market application prospects and are becoming the primary focus of the low-altitude economy; however, with increasing aircraft density, effective planning of reasonable flight paths and avoiding...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/8/12/749 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850041850571784192 |
|---|---|
| author | Fan Liu Pengchuan Wang Aniruddha Bhattacharjya Qianmu Li |
| author_facet | Fan Liu Pengchuan Wang Aniruddha Bhattacharjya Qianmu Li |
| author_sort | Fan Liu |
| collection | DOAJ |
| description | As a central subdivision of the low-altitude economy industry, industrial and consumer drones have broad market application prospects and are becoming the primary focus of the low-altitude economy; however, with increasing aircraft density, effective planning of reasonable flight paths and avoiding conflicts between flight paths have become critical issues in UAV clustering. Current UAV path planning often concentrates on 2D and 3D realistic scenes, which do not meet the actual requirements of realistic spherical paths. This paper has proposed a Gradient-Based Optimization algorithm based on the State Transition function (STGBO) to address the spherical path planning problem for UAV clusters. The state transition function is applied on the scale of medium and high-dimensional cities, balancing the stability and efficiency of the algorithm. Through evolution and comparisons with many mainstream meta-heuristic algorithms, STGBO has demonstrated superior performance and effectiveness in solving Medium-Altitude Unmanned Aerial Vehicle (MUAV) path planning problems on three-dimensional spherical surfaces, contributing to the development of the low-altitude economy. |
| format | Article |
| id | doaj-art-d30b8ea25c104bdcb47361ca14126ce8 |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-d30b8ea25c104bdcb47361ca14126ce82025-08-20T02:55:41ZengMDPI AGDrones2504-446X2024-12-0181274910.3390/drones8120749A Novel Spherical Shortest Path Planning Method for UAVsFan Liu0Pengchuan Wang1Aniruddha Bhattacharjya2Qianmu Li3School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100190, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaAs a central subdivision of the low-altitude economy industry, industrial and consumer drones have broad market application prospects and are becoming the primary focus of the low-altitude economy; however, with increasing aircraft density, effective planning of reasonable flight paths and avoiding conflicts between flight paths have become critical issues in UAV clustering. Current UAV path planning often concentrates on 2D and 3D realistic scenes, which do not meet the actual requirements of realistic spherical paths. This paper has proposed a Gradient-Based Optimization algorithm based on the State Transition function (STGBO) to address the spherical path planning problem for UAV clusters. The state transition function is applied on the scale of medium and high-dimensional cities, balancing the stability and efficiency of the algorithm. Through evolution and comparisons with many mainstream meta-heuristic algorithms, STGBO has demonstrated superior performance and effectiveness in solving Medium-Altitude Unmanned Aerial Vehicle (MUAV) path planning problems on three-dimensional spherical surfaces, contributing to the development of the low-altitude economy.https://www.mdpi.com/2504-446X/8/12/749unmanned aerial vehicle (UAV)spherical path planningparticle swarm optimization (PSO)genetic algorithm (GA)gradient-based optimizergradient-based optimization algorithm based on the state transition function (STGBO) |
| spellingShingle | Fan Liu Pengchuan Wang Aniruddha Bhattacharjya Qianmu Li A Novel Spherical Shortest Path Planning Method for UAVs Drones unmanned aerial vehicle (UAV) spherical path planning particle swarm optimization (PSO) genetic algorithm (GA) gradient-based optimizer gradient-based optimization algorithm based on the state transition function (STGBO) |
| title | A Novel Spherical Shortest Path Planning Method for UAVs |
| title_full | A Novel Spherical Shortest Path Planning Method for UAVs |
| title_fullStr | A Novel Spherical Shortest Path Planning Method for UAVs |
| title_full_unstemmed | A Novel Spherical Shortest Path Planning Method for UAVs |
| title_short | A Novel Spherical Shortest Path Planning Method for UAVs |
| title_sort | novel spherical shortest path planning method for uavs |
| topic | unmanned aerial vehicle (UAV) spherical path planning particle swarm optimization (PSO) genetic algorithm (GA) gradient-based optimizer gradient-based optimization algorithm based on the state transition function (STGBO) |
| url | https://www.mdpi.com/2504-446X/8/12/749 |
| work_keys_str_mv | AT fanliu anovelsphericalshortestpathplanningmethodforuavs AT pengchuanwang anovelsphericalshortestpathplanningmethodforuavs AT aniruddhabhattacharjya anovelsphericalshortestpathplanningmethodforuavs AT qianmuli anovelsphericalshortestpathplanningmethodforuavs AT fanliu novelsphericalshortestpathplanningmethodforuavs AT pengchuanwang novelsphericalshortestpathplanningmethodforuavs AT aniruddhabhattacharjya novelsphericalshortestpathplanningmethodforuavs AT qianmuli novelsphericalshortestpathplanningmethodforuavs |