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在双重环境中,基于群体食的方法用于适应性覆盖路径规划
IEEE transactions on cybernetics
|May 31, 2023
概括
这项研究引入了一种新的基于群体食的双环境覆盖路径规划 (DH-CPP) 方法,用于群体机器人. DH-CPP使机器人能够在有界和无界的环境中有效地导航和覆盖区域,其灵感来自动物食行为.
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
- 群集情报 群集情报 群集情报
背景情况:
- 覆盖路径规划 (CPP) 对于机器人任务,如环境监测和地形测量至关重要.
- 像捕食者-猎物CPP (PPCPP) 这样的现有方法仅限于有界环境,无法处理无界或双环境场景.
- 机器人通常需要在有界的 (例如建筑物) 和无界的 (例如开放的田野) 区域中运行,需要适应性的CPP策略.
研究的目的:
- 为群体机器人提出一种在线适应性CPP方法,能够处理有界和无界环境.
- 开发一种不需要先前了解环境的方法.
- 为了提高群体机器人的多功能性,用于复杂的,现实世界的任务.
主要方法:
- 提出了一种新的方法,灵感来自动物的群体食行为,称为基于群体食的双环境CPP (DH-CPP).
- 通过DH-CPP算法,可以在线适应环境条件.
- 在模拟的双重环境中,以各种障碍物配置来评估性能.
主要成果:
- DH-CPP在处理双重环境方面表现出高效.
- 该方法成功地处理了不同形状和数量的静止和动态障碍物.
- 对比分析显示,DH-CPP的表现优于三种最先进的CPP方法.
结论:
- 拟议的DH-CPP方法为在复杂的双环境环境中运行的群体机器人提供了强大的解决方案.
- 群体食灵感允许进行适应性和高效的覆盖计划.
- 这种方法显著提升了群体机器人的能力,用于需要在已知和未知的地形上进行导航的任务.
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