工业4.0中的可扩展和高能效的任务分配:利用分布式拍卖和IBPSO
Qingwen Li1, Tang Wai Fan1, Lam Sui Kei1
1Department of Construction and Quality Management, School of Science and Technology, Hong Kong Metropolitan University, Homantin Kowloon, Hong Kong SAR, China.
PloS one
|January 17, 2025
概括
本研究介绍了工业4.0智能工厂中机器人任务分配的新算法. 这些方法提高了基础和复杂的协作任务的效率并降低了成本.
科学领域:
- 机器人和自动化 机器人和自动化
- 人工智能的人工智能
- 工业工程 工业工程 工业工程
背景情况:
- 工业4.0在多机器人系统的动态任务分配方面提出了挑战.
- 有效的任务分配对于智能工厂运营和可持续性至关重要.
研究的目的:
- 开发一个分散的拍卖算法 (AOCTA) 以实现灵活的任务分配.
- 为复杂的协作任务引入改进的二进制粒子群集优化 (IBPSO).
- 提高能源效率,降低机器人任务管理中的计算成本.
主要方法:
- 拍卖分权算法 (AOCTA) 的设计,用于动态的任务分配.
- 应用一个改进的二进制粒子群集优化 (IBPSO) 进行多机器人联盟的形成.
- 进行广泛的模拟以验证拟议的方法.
主要成果:
- 在动态环境中,AOCTA确保了高效和灵活的任务分配.
- IBPSO优化了联盟的形成,提高了能源效率,降低了计算成本.
- 模拟表明,在效率,任务完成和可扩展性方面,与传统方法相比,有显著的改进.
结论:
- 提出的方法为智能制造中的动态任务分配提供了一个全面的解决方案.
- 这项研究通过解决运营效率和环境影响来推进可持续的智能制造.
- 开发的算法促进了创新和高效的制造系统的发展.
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