Path Planning for Unmanned Aerial Vehicles in Dynamic Environments: A Novel Approach Using Improved A* and Grey Wolf Optimizer
Unmanned aerial vehicles (UAVs) play pivotal roles in various applications, from surveillance to delivery services. Efficient path planning for UAVs in dynamic environments with obstacles and moving landing stations is essential to ensure safe and reliable operations. In this study, we propose a nov...
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| Language: | English |
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MDPI AG
2024-11-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/11/531 |
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| author | Ali Haidar Ahmad Oussama Zahwe Abbass Nasser Benoit Clement |
| author_facet | Ali Haidar Ahmad Oussama Zahwe Abbass Nasser Benoit Clement |
| author_sort | Ali Haidar Ahmad |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) play pivotal roles in various applications, from surveillance to delivery services. Efficient path planning for UAVs in dynamic environments with obstacles and moving landing stations is essential to ensure safe and reliable operations. In this study, we propose a novel approach that combines the A* algorithm with the grey wolf optimizer (GWO) for path planning, referred to as GW-A*. Our approach enhances the traditional A algorithm by incorporating weighted nodes, where the weights are determined based on the distance from obstacles and further optimized using GWO. A simulation using dynamic factors such as wind direction and wind speed, which affect the quadrotor UAV in the presence of obstacles, was used to test the new approach, and we compared it with the A* algorithm using various heuristics. The results showed that GW-A* outperformed A* in most scenarios with high and low wind speeds, offering more efficient paths and greater adaptability. |
| format | Article |
| id | doaj-art-b8ef3dd129b44f609cb8d589fe4246ea |
| institution | OA Journals |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-b8ef3dd129b44f609cb8d589fe4246ea2025-08-20T02:04:45ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-11-01151153110.3390/wevj15110531Path Planning for Unmanned Aerial Vehicles in Dynamic Environments: A Novel Approach Using Improved A* and Grey Wolf OptimizerAli Haidar Ahmad0Oussama Zahwe1Abbass Nasser2Benoit Clement3Lab-STICC UMR CNRS 6285, ENSTA Bretagne, 29200 Brest, FranceVectrawave Device, 221001 Lannion, FranceHoly-Spirit University of Kaslik (USEK), Jounieh P.O. Box 446, LebanonLab-STICC UMR CNRS 6285, ENSTA Bretagne, 29200 Brest, FranceUnmanned aerial vehicles (UAVs) play pivotal roles in various applications, from surveillance to delivery services. Efficient path planning for UAVs in dynamic environments with obstacles and moving landing stations is essential to ensure safe and reliable operations. In this study, we propose a novel approach that combines the A* algorithm with the grey wolf optimizer (GWO) for path planning, referred to as GW-A*. Our approach enhances the traditional A algorithm by incorporating weighted nodes, where the weights are determined based on the distance from obstacles and further optimized using GWO. A simulation using dynamic factors such as wind direction and wind speed, which affect the quadrotor UAV in the presence of obstacles, was used to test the new approach, and we compared it with the A* algorithm using various heuristics. The results showed that GW-A* outperformed A* in most scenarios with high and low wind speeds, offering more efficient paths and greater adaptability.https://www.mdpi.com/2032-6653/15/11/531unmanned aerial vehicleA* algorithmgrey wolf optimizerpath planningweighted graph |
| spellingShingle | Ali Haidar Ahmad Oussama Zahwe Abbass Nasser Benoit Clement Path Planning for Unmanned Aerial Vehicles in Dynamic Environments: A Novel Approach Using Improved A* and Grey Wolf Optimizer World Electric Vehicle Journal unmanned aerial vehicle A* algorithm grey wolf optimizer path planning weighted graph |
| title | Path Planning for Unmanned Aerial Vehicles in Dynamic Environments: A Novel Approach Using Improved A* and Grey Wolf Optimizer |
| title_full | Path Planning for Unmanned Aerial Vehicles in Dynamic Environments: A Novel Approach Using Improved A* and Grey Wolf Optimizer |
| title_fullStr | Path Planning for Unmanned Aerial Vehicles in Dynamic Environments: A Novel Approach Using Improved A* and Grey Wolf Optimizer |
| title_full_unstemmed | Path Planning for Unmanned Aerial Vehicles in Dynamic Environments: A Novel Approach Using Improved A* and Grey Wolf Optimizer |
| title_short | Path Planning for Unmanned Aerial Vehicles in Dynamic Environments: A Novel Approach Using Improved A* and Grey Wolf Optimizer |
| title_sort | path planning for unmanned aerial vehicles in dynamic environments a novel approach using improved a and grey wolf optimizer |
| topic | unmanned aerial vehicle A* algorithm grey wolf optimizer path planning weighted graph |
| url | https://www.mdpi.com/2032-6653/15/11/531 |
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