Dynamic obstacle avoidance scheduling strategy for AGVs in dual-layer production lines considering task completion time and energy consumption.
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| Title: | Dynamic obstacle avoidance scheduling strategy for AGVs in dual-layer production lines considering task completion time and energy consumption. |
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| Authors: | Chen, Yiqiao, Chen, Haiduo, Chen, Shimiao, Guo, Jian, Huang, Deqiang, Kang, Chao |
| Source: | Production Engineering (09446524); Dec2025, Vol. 19 Issue 6, p1139-1162, 24p |
| Abstract: | The scheduling of Automated Guided Vehicles (AGVs) in relay welding production lines presents significant challenges due to the complexity of task allocation and path planning. Task completion time and energy consumption are closely interrelated; inefficient scheduling can result in excessive energy consumption, thereby reducing system efficiency and battery lifespan, especially under continuous multi-task conditions. To tackle these challenges, this paper proposes a novel multi-AGV cooperative scheduling strategy that achieves an optimal balance between task completion time and energy consumption. Specifically, the proposed approach addresses cross-floor scheduling issues in dual-layer relay welding production lines, where AGVs must frequently traverse between levels using elevators. First, we develop a scheduling model with segmented optimization for vertical movement considering time consumption. Then, we introduce a dynamic collision avoidance mechanism, incorporating an improved BUG2 and A* algorithm for AGV motion planning. Further, a comparative analysis of Dijkstra algorithm, A*, and genetic algorithms is conducted to validate the proposed scheduling strategy. Our A* approach achieves a significant objective function improvement of over 16% compared to the Genetic Algorithm at 15 tasks. Comprehensive simulations under varying task volumes, battery levels, and AGV speeds demonstrate the robustness of the approach. The proposed strategy consistently outperforms baseline algorithms in achieving an efficient trade-off between operational time and energy usage. This contributes to more sustainable and intelligent production scheduling in practical industrial environments. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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