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多机器人探索使用先进的多目标salp群算法,以提供高效的覆盖和性能.

Ali El Romeh1, Seyedali Mirjalili2,3,4

  • 1Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Melbourne, 3000, Australia.

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概括
此摘要是机器生成的。

先进的多目标Salp Swarm算法探索技术 (AMET) 通过平衡效率和准确性来改善多机器人探索. 在复杂任务的覆盖面和协调方面,AMET的性能优于现有的方法.

关键词:
覆盖面积 覆盖面积 覆盖面积人工智能的人工智能自主机器人系统 自主机器人系统计算效率 计算效率 计算效率多目标优化多目标优化多机器人探索探索萨尔普群群算法 萨尔普群群算法 萨尔普群群算法

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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
  • 优化算法 优化算法

背景情况:

  • 多机器人探索对于搜救和环境监测等任务至关重要.
  • 现有的方法往往难以平衡勘探效率与绘图精度.
  • 需要适应性搜索策略来提高复杂环境中的稳定性.

研究的目的:

  • 引入先进的多目标Salp Swarm算法探索技术 (AMET) 进行增强的多机器人探索.
  • 开发一个框架,将决定性协调与自适应的多目标优化整合在一起.
  • 在多机器人系统中,提高勘探效率和绘图精度之间的权衡.

主要方法:

  • 亚美特将协调多机器人勘探 (CME) 与多目标沙尔普群算法 (MSSA) 结合起来.
  • 对各种单一目标和多目标战略 (CME-MGWO,CME-MACO,CME-MODA,CME-SSA) 的表现进行了评估.
  • 关键指标包括运行时间效率,区域覆盖,任务弹性和冗余减少.

主要成果:

  • 与现有方法相比,AMET在所有评估指标上表现出卓越的表现.
  • 该技术实现了显著更好的区域覆盖,并减少了计算开销.
  • 观察到加强了勘探协调和任务完成弹性.

结论:

  • AMET为多机器人探索提供了一个可扩展和高效的方法.
  • 该框架为平衡勘探效率和绘图准确性提供了一个强大的解决方案.
  • 在搜救,行星探索和环境监测方面,AMET具有潜在的应用.