针对高密度停车环境的多机器人路径规划,考虑效率和公平性
1Department of Mechanical Engineering, Korea University, Seoul 02841, Republic of Korea.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究引入了一个新的自主停车系统,用于高密度停车场. 它平衡了运营效率和任务优先级,通过多个机器人有效地管理高峰期的需求.
科学领域:
- 机器人和自动化机器人与自动化
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 机场停车拥堵需要像高密度停车系统 (HDP) 这样的高效解决方案.
- 传统的多代理路径查找 (MAPF) 方法优先考虑整体效率,而不是单个任务优先级和机器人的可行性.
- 现有的MAPF方法为停车机器人产生了物理不可行的路径.
研究的目的:
- 开发一种多机器人路径规划方法,平衡运营效率和个别任务优先级.
- 在现实世界机场停车场场景中解决传统MAPF的局限性.
- 创建基于时间的停车操作和机器人车队估计的实际基础.
主要方法:
- 提出了一种新的多机器人路径规划方法,增强基于优先级的搜索 (PBS).
- 动态调整优先级,以确保整个系统的效率和个别车辆的任务完成.
- 实现了一个模拟器,复制了一个真实的机场停车环境,有100个车位和停车机器人.
主要成果:
- 自主停车系统有效地利用真实机场数据处理高峰季停车需求.
- 在优化的重量值下,实现了超过每小时41辆的吞吐量.
- 在完成任务时表现出可接受的公平性,同时满足高需求.
结论:
- 拟议的方法为高效和优先意识的自动停车提供了一个实际的解决方案.
- 验证了系统在复杂的机场环境中管理高峰季度需求的能力.
- 提供了优化停车操作和确定最佳机器人部署的基础.
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