Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence
With the widespread use of autonomous guided vehicles (AGVs), avoiding collisions has become a challenging problem. Addressing the issue is not straightforward since production efficiency, collision avoidance, and energy consumption are conflicting factors. This paper proposes a novel edge computing...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8044 |
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| author | Zhengying Cai Jingshu Du Tianhao Huang Zhuimeng Lu Zeya Liu Guoqiang Gong |
| author_facet | Zhengying Cai Jingshu Du Tianhao Huang Zhuimeng Lu Zeya Liu Guoqiang Gong |
| author_sort | Zhengying Cai |
| collection | DOAJ |
| description | With the widespread use of autonomous guided vehicles (AGVs), avoiding collisions has become a challenging problem. Addressing the issue is not straightforward since production efficiency, collision avoidance, and energy consumption are conflicting factors. This paper proposes a novel edge computing method based on vehicle edge intelligence to solve the energy-efficient collision-free machine/AGV scheduling problem. First, a vehicle edge intelligence architecture was built, and the corresponding state transition diagrams for collision-free scheduling were developed. Second, the energy-efficient collision-free machine/AGV scheduling problem was modeled as a multi-objective function with electric capacity constraints, where production efficiency, collision prevention, and energy conservation were comprehensively considered. Third, an artificial plant community algorithm was explored based on the edge intelligence of AGVs. The proposed method utilizes a heuristic search and the swarm intelligence of multiple AGVs to realize energy-efficient collision-free scheduling and is suitable for deploying on embedded platforms for edge computing. Finally, a benchmark dataset was developed, and some benchmark experiments were conducted, where the results revealed that the proposed heuristic method could effectively instruct multiple automatic guided vehicles to avoid collisions with high energy efficiency. |
| format | Article |
| id | doaj-art-3f3244743c694fd8a0d0bbf5473b9823 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3f3244743c694fd8a0d0bbf5473b98232024-12-27T14:52:49ZengMDPI AGSensors1424-82202024-12-012424804410.3390/s24248044Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge IntelligenceZhengying Cai0Jingshu Du1Tianhao Huang2Zhuimeng Lu3Zeya Liu4Guoqiang Gong5Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, ChinaHubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, ChinaHubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, ChinaHubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, ChinaHubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, ChinaHubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, ChinaWith the widespread use of autonomous guided vehicles (AGVs), avoiding collisions has become a challenging problem. Addressing the issue is not straightforward since production efficiency, collision avoidance, and energy consumption are conflicting factors. This paper proposes a novel edge computing method based on vehicle edge intelligence to solve the energy-efficient collision-free machine/AGV scheduling problem. First, a vehicle edge intelligence architecture was built, and the corresponding state transition diagrams for collision-free scheduling were developed. Second, the energy-efficient collision-free machine/AGV scheduling problem was modeled as a multi-objective function with electric capacity constraints, where production efficiency, collision prevention, and energy conservation were comprehensively considered. Third, an artificial plant community algorithm was explored based on the edge intelligence of AGVs. The proposed method utilizes a heuristic search and the swarm intelligence of multiple AGVs to realize energy-efficient collision-free scheduling and is suitable for deploying on embedded platforms for edge computing. Finally, a benchmark dataset was developed, and some benchmark experiments were conducted, where the results revealed that the proposed heuristic method could effectively instruct multiple automatic guided vehicles to avoid collisions with high energy efficiency.https://www.mdpi.com/1424-8220/24/24/8044automatic guided vehiclesenergy efficientcollision-free schedulingvehicle edge intelligenceartificial plant community algorithm |
| spellingShingle | Zhengying Cai Jingshu Du Tianhao Huang Zhuimeng Lu Zeya Liu Guoqiang Gong Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence Sensors automatic guided vehicles energy efficient collision-free scheduling vehicle edge intelligence artificial plant community algorithm |
| title | Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence |
| title_full | Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence |
| title_fullStr | Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence |
| title_full_unstemmed | Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence |
| title_short | Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence |
| title_sort | energy efficient collision free machine agv scheduling using vehicle edge intelligence |
| topic | automatic guided vehicles energy efficient collision-free scheduling vehicle edge intelligence artificial plant community algorithm |
| url | https://www.mdpi.com/1424-8220/24/24/8044 |
| work_keys_str_mv | AT zhengyingcai energyefficientcollisionfreemachineagvschedulingusingvehicleedgeintelligence AT jingshudu energyefficientcollisionfreemachineagvschedulingusingvehicleedgeintelligence AT tianhaohuang energyefficientcollisionfreemachineagvschedulingusingvehicleedgeintelligence AT zhuimenglu energyefficientcollisionfreemachineagvschedulingusingvehicleedgeintelligence AT zeyaliu energyefficientcollisionfreemachineagvschedulingusingvehicleedgeintelligence AT guoqianggong energyefficientcollisionfreemachineagvschedulingusingvehicleedgeintelligence |