Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach
Automated guided vehicles (AGVs) are vital for optimizing the transport of material in modern industry. AGVs have been widely used in production, logistics, transportation, and commerce, enhancing productivity, lowering labor costs, improving energy efficiency, and ensuring safety. However, path pla...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/1/35 |
| _version_ | 1797344561141383168 |
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| author | Thi-Kien Dao Truong-Giang Ngo Jeng-Shyang Pan Thi-Thanh-Tan Nguyen Trong-The Nguyen |
| author_facet | Thi-Kien Dao Truong-Giang Ngo Jeng-Shyang Pan Thi-Thanh-Tan Nguyen Trong-The Nguyen |
| author_sort | Thi-Kien Dao |
| collection | DOAJ |
| description | Automated guided vehicles (AGVs) are vital for optimizing the transport of material in modern industry. AGVs have been widely used in production, logistics, transportation, and commerce, enhancing productivity, lowering labor costs, improving energy efficiency, and ensuring safety. However, path planning for AGVs in complex and dynamic environments remains challenging due to the computation of obstacle avoidance and efficient transport. This study proposes a novel approach that combines multi-objective particle swarm optimization (MOPSO) and the dynamic-window approach (DWA) to enhance AGV path planning. Optimal AGV trajectories considering energy consumption, travel time, and collision avoidance were used to model the multi-objective functions for dealing with the outcome-feasible optimal solution. Empirical findings and results demonstrate the approach’s effectiveness and efficiency, highlighting its potential for improving AGV navigation in real-world scenarios. |
| first_indexed | 2024-03-08T11:04:30Z |
| format | Article |
| id | doaj.art-8a605938d21d4f3faafdc784f70a4d0d |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-08T11:04:30Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-8a605938d21d4f3faafdc784f70a4d0d2024-01-26T15:15:54ZengMDPI AGBiomimetics2313-76732024-01-01913510.3390/biomimetics9010035Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window ApproachThi-Kien Dao0Truong-Giang Ngo1Jeng-Shyang Pan2Thi-Thanh-Tan Nguyen3Trong-The Nguyen4Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, ChinaFaculty of Computer Science and Engineering, Thuyloi University, Hanoi 116705, VietnamCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, ChinaFaculty of Information Technology, Electric Power University, Hanoi 100000, VietnamFujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, ChinaAutomated guided vehicles (AGVs) are vital for optimizing the transport of material in modern industry. AGVs have been widely used in production, logistics, transportation, and commerce, enhancing productivity, lowering labor costs, improving energy efficiency, and ensuring safety. However, path planning for AGVs in complex and dynamic environments remains challenging due to the computation of obstacle avoidance and efficient transport. This study proposes a novel approach that combines multi-objective particle swarm optimization (MOPSO) and the dynamic-window approach (DWA) to enhance AGV path planning. Optimal AGV trajectories considering energy consumption, travel time, and collision avoidance were used to model the multi-objective functions for dealing with the outcome-feasible optimal solution. Empirical findings and results demonstrate the approach’s effectiveness and efficiency, highlighting its potential for improving AGV navigation in real-world scenarios.https://www.mdpi.com/2313-7673/9/1/35automated guided vehiclespath planningmulti-object PSOdynamic-window approachcollision avoidanceenergy consumption |
| spellingShingle | Thi-Kien Dao Truong-Giang Ngo Jeng-Shyang Pan Thi-Thanh-Tan Nguyen Trong-The Nguyen Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach Biomimetics automated guided vehicles path planning multi-object PSO dynamic-window approach collision avoidance energy consumption |
| title | Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach |
| title_full | Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach |
| title_fullStr | Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach |
| title_full_unstemmed | Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach |
| title_short | Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach |
| title_sort | enhancing path planning capabilities of automated guided vehicles in dynamic environments multi objective pso and dynamic window approach |
| topic | automated guided vehicles path planning multi-object PSO dynamic-window approach collision avoidance energy consumption |
| url | https://www.mdpi.com/2313-7673/9/1/35 |
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