Improved Grey Wolf Algorithm: A Method for UAV Path Planning
The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path plann...
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MDPI AG
2024-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/11/675 |
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| author | Xingyu Zhou Guoqing Shi Jiandong Zhang |
| author_facet | Xingyu Zhou Guoqing Shi Jiandong Zhang |
| author_sort | Xingyu Zhou |
| collection | DOAJ |
| description | The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. We propose a collaborative UAV path planning model that includes constraint analysis and an evaluation function. Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. In the final stage, a UAV path planning simulation platform is employed to evaluate and compare the effectiveness of the original and improved algorithms. Simulation results demonstrate that the proposed NI–GWO algorithm can effectively solve the path planning problem for UAVs in uncertain environments. Compared to Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, and MP–GWO algorithms, the NI–GWO algorithm can achieve the optimal fitness value and has significant advantages in terms of average path length, time, number of collisions, and obstacle avoidance capabilities. |
| format | Article |
| id | doaj-art-9db6c3aa1c754dccb8a85d90c63ac75e |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-9db6c3aa1c754dccb8a85d90c63ac75e2024-11-26T18:00:48ZengMDPI AGDrones2504-446X2024-11-0181167510.3390/drones8110675Improved Grey Wolf Algorithm: A Method for UAV Path PlanningXingyu Zhou0Guoqing Shi1Jiandong Zhang2School of Electronic Information, Northwestern Polytechnic University, Xi’an 710129, ChinaSchool of Electronic Information, Northwestern Polytechnic University, Xi’an 710129, ChinaSchool of Electronic Information, Northwestern Polytechnic University, Xi’an 710129, ChinaThe Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. We propose a collaborative UAV path planning model that includes constraint analysis and an evaluation function. Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. In the final stage, a UAV path planning simulation platform is employed to evaluate and compare the effectiveness of the original and improved algorithms. Simulation results demonstrate that the proposed NI–GWO algorithm can effectively solve the path planning problem for UAVs in uncertain environments. Compared to Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, and MP–GWO algorithms, the NI–GWO algorithm can achieve the optimal fitness value and has significant advantages in terms of average path length, time, number of collisions, and obstacle avoidance capabilities.https://www.mdpi.com/2504-446X/8/11/675multi-agent systemsUAV navigationplanningintelligent controlartificial intelligence |
| spellingShingle | Xingyu Zhou Guoqing Shi Jiandong Zhang Improved Grey Wolf Algorithm: A Method for UAV Path Planning Drones multi-agent systems UAV navigation planning intelligent control artificial intelligence |
| title | Improved Grey Wolf Algorithm: A Method for UAV Path Planning |
| title_full | Improved Grey Wolf Algorithm: A Method for UAV Path Planning |
| title_fullStr | Improved Grey Wolf Algorithm: A Method for UAV Path Planning |
| title_full_unstemmed | Improved Grey Wolf Algorithm: A Method for UAV Path Planning |
| title_short | Improved Grey Wolf Algorithm: A Method for UAV Path Planning |
| title_sort | improved grey wolf algorithm a method for uav path planning |
| topic | multi-agent systems UAV navigation planning intelligent control artificial intelligence |
| url | https://www.mdpi.com/2504-446X/8/11/675 |
| work_keys_str_mv | AT xingyuzhou improvedgreywolfalgorithmamethodforuavpathplanning AT guoqingshi improvedgreywolfalgorithmamethodforuavpathplanning AT jiandongzhang improvedgreywolfalgorithmamethodforuavpathplanning |