Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic Environments
Accurate path planning is essential for effective regional avoidance in multiple unmanned aerial vehicle (multi-UAV) systems. Existing static path-planning techniques often fail to integrate multiple information sources, resulting in diminished performance in information-rich and dynamic environment...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/9/1/38 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588609968930816 |
|---|---|
| author | Pengfei Duan Dawei Chen |
| author_facet | Pengfei Duan Dawei Chen |
| author_sort | Pengfei Duan |
| collection | DOAJ |
| description | Accurate path planning is essential for effective regional avoidance in multiple unmanned aerial vehicle (multi-UAV) systems. Existing static path-planning techniques often fail to integrate multiple information sources, resulting in diminished performance in information-rich and dynamic environments. This paper proposes a distributed collaborative path-planning algorithm for dynamically changing targets in complex environments with multisource information. More specifically, a multi-UAV collaboration and path-planning method based on information-fusion technology is first presented to fuse the multisource data received by the UAVs from different platforms, such as space-based, air-based, and land-based. Subsequently, we introduce an algorithm to mark and divide the environment and hazardous areas, therefore enhancing overall situational awareness and eliminating visual blind spots in emergency communications scenarios. Furthermore, we develop an efficient, intelligent path-planning algorithm founded on objective functions and optimization methods at different stages, enabling UAVs to navigate safely while minimizing energy expenditure. Finally, the proposed strategy is validated through a simulation platform, demonstrating that the intelligent path-planning algorithm introduced in this study exhibits robust trajectory optimization capabilities in complex environments enriched with diverse information and potential threats. |
| format | Article |
| id | doaj-art-242b530359344dc588499f237e70d5b2 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-242b530359344dc588499f237e70d5b22025-01-24T13:29:44ZengMDPI AGDrones2504-446X2025-01-01913810.3390/drones9010038Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic EnvironmentsPengfei Duan0Dawei Chen1Department of Artificial Intelligence, Beihang University, Beijing 100191, ChinaCollaborative Innovation Center, Chinese Aeronautical Establishment, Beijing 100012, ChinaAccurate path planning is essential for effective regional avoidance in multiple unmanned aerial vehicle (multi-UAV) systems. Existing static path-planning techniques often fail to integrate multiple information sources, resulting in diminished performance in information-rich and dynamic environments. This paper proposes a distributed collaborative path-planning algorithm for dynamically changing targets in complex environments with multisource information. More specifically, a multi-UAV collaboration and path-planning method based on information-fusion technology is first presented to fuse the multisource data received by the UAVs from different platforms, such as space-based, air-based, and land-based. Subsequently, we introduce an algorithm to mark and divide the environment and hazardous areas, therefore enhancing overall situational awareness and eliminating visual blind spots in emergency communications scenarios. Furthermore, we develop an efficient, intelligent path-planning algorithm founded on objective functions and optimization methods at different stages, enabling UAVs to navigate safely while minimizing energy expenditure. Finally, the proposed strategy is validated through a simulation platform, demonstrating that the intelligent path-planning algorithm introduced in this study exhibits robust trajectory optimization capabilities in complex environments enriched with diverse information and potential threats.https://www.mdpi.com/2504-446X/9/1/38distributed collaborationpath planningartificial intelligenceinformation fusionsearch algorithms |
| spellingShingle | Pengfei Duan Dawei Chen Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic Environments Drones distributed collaboration path planning artificial intelligence information fusion search algorithms |
| title | Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic Environments |
| title_full | Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic Environments |
| title_fullStr | Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic Environments |
| title_full_unstemmed | Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic Environments |
| title_short | Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic Environments |
| title_sort | distributed cooperative path planning for multi uav in information rich and dynamic environments |
| topic | distributed collaboration path planning artificial intelligence information fusion search algorithms |
| url | https://www.mdpi.com/2504-446X/9/1/38 |
| work_keys_str_mv | AT pengfeiduan distributedcooperativepathplanningformultiuavininformationrichanddynamicenvironments AT daweichen distributedcooperativepathplanningformultiuavininformationrichanddynamicenvironments |