Conference proceeding
An Enhanced Collision-Aware Min-Max A Path Planning Algorithm (ECMMA) in Automated Calibration Production Lines
2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS), pp.365-368
07/07/2023
DOI: 10.1109/ISCTIS58954.2023.10213017
Abstract
In actual production, the collisions between components and surrounding objects often occur on automatic calibration lines. To solve these problems, an enhanced collision-aware min-max A* path planning algorithm (ECMMA*) based on the spatial and temporal features of component locations is proposed in this paper. ECMMA* determines in real time whether there is interference between the running path of the components and the coordinates of the surrounding objects. It also takes into account overall costs to optimize the path planning for automated calibration production lines. The experimental results show that the proposed method effectively reduces the distance and time of equipment movement, improves production efficiency and resource utilization, and ensures the effectiveness and safety of the paths.
Details
- Title: Subtitle
- An Enhanced Collision-Aware Min-Max A Path Planning Algorithm (ECMMA) in Automated Calibration Production Lines
- Creators
- Siying Li - Zhejiang University of Finance and EconomicsLing Zhu - Zhejiang University of Finance and EconomicsDongyan Wang - Zhejiang University of Finance and EconomicsYutian Cen - Zhejiang University of Finance and EconomicsGuangyu Liu - Hangzhou Dianzi UniversityWeiguo Fan - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS), pp.365-368
- Publisher
- IEEE
- DOI
- 10.1109/ISCTIS58954.2023.10213017
- Grant note
- 62002315,62273124 / National Natural Science Foundation of China (10.13039/501100001809) HZKY20220197 / Ministry of Education Chunhui Program Cooperative Research Project of China (10.13039/501100002338)
- Language
- English
- Date published
- 07/07/2023
- Academic Unit
- Business Analytics
- Record Identifier
- 9984459640602771
Metrics
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