Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework
This study evaluates and compares the computational performance and practical applicability of advanced path planning algorithms for Unmanned Aerial Vehicles (UAVs) in dynamic and obstacle-rich environments. The Adaptive Multi-Objective Path Planning (AMOPP) framework is highlighted for its ability...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Designs |
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| Online Access: | https://www.mdpi.com/2411-9660/8/6/136 |
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| author | Gregorius Airlangga Ronald Sukwadi Widodo Widjaja Basuki Lai Ferry Sugianto Oskar Ika Adi Nugroho Yoel Kristian Radyan Rahmananta |
| author_facet | Gregorius Airlangga Ronald Sukwadi Widodo Widjaja Basuki Lai Ferry Sugianto Oskar Ika Adi Nugroho Yoel Kristian Radyan Rahmananta |
| author_sort | Gregorius Airlangga |
| collection | DOAJ |
| description | This study evaluates and compares the computational performance and practical applicability of advanced path planning algorithms for Unmanned Aerial Vehicles (UAVs) in dynamic and obstacle-rich environments. The Adaptive Multi-Objective Path Planning (AMOPP) framework is highlighted for its ability to balance multiple objectives, including path length, smoothness, collision avoidance, and real-time responsiveness. Through experimental analysis, AMOPP demonstrates superior performance, with a 15% reduction in path length compared to A*, achieving an average path length of 450 m. Its angular deviation of 8.0° ensures smoother trajectories than traditional methods like Genetic Algorithm and Particle Swarm Optimization (PSO). Moreover, AMOPP achieves a 0% collision rate across all simulations, surpassing heuristic-based methods like Cuckoo Search and Bee Colony Optimization, which exhibit higher collision rates. Real-time responsiveness is another key strength of AMOPP, with an average re-planning time of 0.75 s, significantly outperforming A* and RRT*. The computational complexities of each algorithm are analyzed, with AMOPP exhibiting a time complexity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>k</mi><mo>·</mo><mi>n</mi><mo>)</mo></mrow></semantics></math></inline-formula> and a space complexity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>n</mi><mo>)</mo></mrow></semantics></math></inline-formula>, ensuring scalability and efficiency for large-scale operations. The study also presents a comprehensive qualitative and quantitative comparison of 14 algorithms using 3D visualizations, highlighting their strengths, limitations, and suitable application scenarios. By integrating weighted optimization with penalty-based strategies and spline interpolation, AMOPP provides a robust solution for UAV path planning, particularly in scenarios requiring smooth navigation and adaptive re-planning. This work establishes AMOPP as a promising framework for real-time, efficient, and safe UAV operations in dynamic environments. |
| format | Article |
| id | doaj-art-bdf9d4969429412da65a615f37ad0d91 |
| institution | DOAJ |
| issn | 2411-9660 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Designs |
| spelling | doaj-art-bdf9d4969429412da65a615f37ad0d912025-08-20T02:53:19ZengMDPI AGDesigns2411-96602024-12-018613610.3390/designs8060136Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective FrameworkGregorius Airlangga0Ronald Sukwadi1Widodo Widjaja Basuki2Lai Ferry Sugianto3Oskar Ika Adi Nugroho4Yoel Kristian5Radyan Rahmananta6Department of Information Systems, Atma Jaya Catholic University of Indonesia, Jakarta 12930, IndonesiaDepartment of Industrial Engineering, Atma Jaya Catholic University of Indonesia, Jakarta 12930, IndonesiaDepartment of Mechanical Engineering, Atma Jaya Catholic University of Indonesia, Jakarta 12930, IndonesiaDepartment of Business Administration, Fujen Catholic University, Taipei 24205, TaiwanDepartment of Electrical Engineering, National Chung Cheng University, Chiayi 621301, TaiwanDepartment of Information Systems, Atma Jaya Catholic University of Indonesia, Jakarta 12930, IndonesiaDepartment of Information Systems, Atma Jaya Catholic University of Indonesia, Jakarta 12930, IndonesiaThis study evaluates and compares the computational performance and practical applicability of advanced path planning algorithms for Unmanned Aerial Vehicles (UAVs) in dynamic and obstacle-rich environments. The Adaptive Multi-Objective Path Planning (AMOPP) framework is highlighted for its ability to balance multiple objectives, including path length, smoothness, collision avoidance, and real-time responsiveness. Through experimental analysis, AMOPP demonstrates superior performance, with a 15% reduction in path length compared to A*, achieving an average path length of 450 m. Its angular deviation of 8.0° ensures smoother trajectories than traditional methods like Genetic Algorithm and Particle Swarm Optimization (PSO). Moreover, AMOPP achieves a 0% collision rate across all simulations, surpassing heuristic-based methods like Cuckoo Search and Bee Colony Optimization, which exhibit higher collision rates. Real-time responsiveness is another key strength of AMOPP, with an average re-planning time of 0.75 s, significantly outperforming A* and RRT*. The computational complexities of each algorithm are analyzed, with AMOPP exhibiting a time complexity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>k</mi><mo>·</mo><mi>n</mi><mo>)</mo></mrow></semantics></math></inline-formula> and a space complexity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>n</mi><mo>)</mo></mrow></semantics></math></inline-formula>, ensuring scalability and efficiency for large-scale operations. The study also presents a comprehensive qualitative and quantitative comparison of 14 algorithms using 3D visualizations, highlighting their strengths, limitations, and suitable application scenarios. By integrating weighted optimization with penalty-based strategies and spline interpolation, AMOPP provides a robust solution for UAV path planning, particularly in scenarios requiring smooth navigation and adaptive re-planning. This work establishes AMOPP as a promising framework for real-time, efficient, and safe UAV operations in dynamic environments.https://www.mdpi.com/2411-9660/8/6/136multi UAVmulti path planningpath planningmulti-objectiveA* |
| spellingShingle | Gregorius Airlangga Ronald Sukwadi Widodo Widjaja Basuki Lai Ferry Sugianto Oskar Ika Adi Nugroho Yoel Kristian Radyan Rahmananta Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework Designs multi UAV multi path planning path planning multi-objective A* |
| title | Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework |
| title_full | Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework |
| title_fullStr | Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework |
| title_full_unstemmed | Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework |
| title_short | Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework |
| title_sort | adaptive path planning for multi uav systems in dynamic 3d environments a multi objective framework |
| topic | multi UAV multi path planning path planning multi-objective A* |
| url | https://www.mdpi.com/2411-9660/8/6/136 |
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