在拥挤的集体中实现噪音支持的目标实现
Lucy Liu1, Justin Werfel1, Federico Toschi2,3
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
概括
将受控的随机性添加到代理运动中,可以防止拥挤空间的交通堵塞. 这项研究优化了代理密度和噪音,以更快地实现目标,这对于机器人群和城市规划至关重要.
科学领域:
- 集体行为 集体行为
- 机器人技术 机器人技术 机器人技术
- 交通流动动态 交通流动动态
背景情况:
- 在拥挤的环境中导航对于人类和机器人代理人来说至关重要.
- 了解交通流是有效的运动和系统设计的关键.
研究的目的:
- 调查如何在代理运动中引入随机性 (随机性) 影响交通堵塞和目标实现.
- 在拥挤的模拟和现实环境中确定最佳的剂量密度和噪声水平,以最大限度地实现目标.
主要方法:
- 利用计算模拟来模拟代理行为和集体动态.
- 开发了对群目标实现率的分析近似方法.
- 进行机器人实验以验证模拟和理论发现.
主要成果:
- 发现运动随机性的关键水平可以防止持续的交通拥堵.
- 分析模型预测和实验证实了最佳的剂量密度和噪声水平,以实现有效的导航.
- 简单的,分散的导航策略在中等密度下被证明是计算效率高和有效的.
结论:
- 运动中的随机性是减轻密集环境中的交通拥堵的可行策略.
- 分散的导航方法为机器人群的复杂中央规划提供了一个有希望的,高效的替代方案.
- 这种跨学科的方法为新出现的交通现象提供了新的见解.
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