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The HoneyComb Paradigm for Research on Collective Human Behavior
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基于局部交互的三维多元代理食策略
1System Engineering Department, Sejong University, Seoul 05006, Republic of Korea.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究介绍了一种新的3D多代理食策略,用于在未知,杂乱的环境中的自主机器人. 该系统有效地检测和收集资源,没有全球本地化,在模拟中表现出卓越的性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 多代理系统 多代理系统
背景情况:
- 多代理系统越来越多地用于复杂的任务,如资源收集.
- 在有障碍的未知的3D环境中进行自主导航具有重大挑战.
- 现有的食策略通常依赖于全球本地化,这限制了它们的适用性.
研究的目的:
- 为在未知的,杂乱的工作空间中为自主代理开发一种新的3D多代理食策略.
- 为了实现有效的资源检测和收集,而无需全球本地化.
- 确保在工作空间内可证明完全检测圆盘.
主要方法:
- 一个两步食策略:1. 在3D工作空间中检测到所有资源 (pucks) 的完整检测. 2. 2. 2. 这是一个很棒的节目. 从基点到每个盘子收集的路径生成.
- 利用本地互动为代理指导和资源运输,消除了全球本地化需求.
- 采用经纪人对代理人的指导沿着路径,以促进盘子的取回和送达基地.
主要成果:
- 拟议的策略成功地使多个代理人在3D杂乱,未知的环境中进行搜索和运输.
- 搜索策略被证明在检测所有盘子时可以被证明是完整的.
- 马特拉布模拟证实了拟议的多代理食策略的表现优异.
结论:
- 开发的基于本地互动的策略为在具有挑战性的环境中进行3D多剂食提供了强大的解决方案.
- 该系统在没有全球本地化的情况下运行的能力提高了其实际适用性.
- 可以证明完整的检测机制确保了全面的资源收集.
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