A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments
Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. In this paper, we p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/4/246 |
| _version_ | 1797605733341069312 |
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| author | Yue Wang Xinpeng Li Xing Zhuang Fanyu Li Yutao Liang |
| author_facet | Yue Wang Xinpeng Li Xing Zhuang Fanyu Li Yutao Liang |
| author_sort | Yue Wang |
| collection | DOAJ |
| description | Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. In this paper, we propose a distributed unknown environment exploration framework for a UAV cluster that comprehensively considers the path and terminal state gain, which is called the Distributed Next-Best-Path and Terminal (DNBPT) method. This method calculates the gain by comprehensively calculating the new exploration grid brought by the exploration path and the guidance of the terminal state to the unexplored area to guide the UAV’s next decision. We propose a suitable multistep selective sampling method and an improved Discrete Binary Particle Swarm Optimization algorithm for path optimization. The simulation results show that the DNBPT can realize rapid exploration under high coverage conditions in multiple scenes. |
| first_indexed | 2024-03-11T05:05:16Z |
| format | Article |
| id | doaj.art-9eb40051a3194062935ab45d7585a31c |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T05:05:16Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-9eb40051a3194062935ab45d7585a31c2023-11-17T18:58:03ZengMDPI AGDrones2504-446X2023-04-017424610.3390/drones7040246A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown EnvironmentsYue Wang0Xinpeng Li1Xing Zhuang2Fanyu Li3Yutao Liang4School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaRapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. In this paper, we propose a distributed unknown environment exploration framework for a UAV cluster that comprehensively considers the path and terminal state gain, which is called the Distributed Next-Best-Path and Terminal (DNBPT) method. This method calculates the gain by comprehensively calculating the new exploration grid brought by the exploration path and the guidance of the terminal state to the unexplored area to guide the UAV’s next decision. We propose a suitable multistep selective sampling method and an improved Discrete Binary Particle Swarm Optimization algorithm for path optimization. The simulation results show that the DNBPT can realize rapid exploration under high coverage conditions in multiple scenes.https://www.mdpi.com/2504-446X/7/4/246exploration of unknown environmentUAV clustersampling and optimizationdistributed path planningparticle swarm optimization |
| spellingShingle | Yue Wang Xinpeng Li Xing Zhuang Fanyu Li Yutao Liang A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments Drones exploration of unknown environment UAV cluster sampling and optimization distributed path planning particle swarm optimization |
| title | A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments |
| title_full | A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments |
| title_fullStr | A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments |
| title_full_unstemmed | A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments |
| title_short | A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments |
| title_sort | sampling based distributed exploration method for uav cluster in unknown environments |
| topic | exploration of unknown environment UAV cluster sampling and optimization distributed path planning particle swarm optimization |
| url | https://www.mdpi.com/2504-446X/7/4/246 |
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