能源感知传感器融合架构用于在受限制环境中的自主通道机器人导航.
Mohamed Shili1, Hicham Chaoui2,3, Khaled Nouri4
1Innovation of Communicant and Cooperative Mobiles Laboratory, National Engineering School of Cartahage, Ariana 2035, Tunisia.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究介绍了一种能量的自适应性传感器融合系统,用于在狭窄道中导航的自主机器人. 该框架可以减少35%的能源消耗,同时保持导航准确度,从而实现更长时间的运行.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 自主系统 自主系统
- 传感器融合式传感器
背景情况:
- 在狭窄道中的自主机器人面临着诸如空间有限,动态障碍和能源限制等挑战.
- 目前的传感器融合方法由于不断激活传感器而耗费大量功率,从而限制了运行时间.
- 管道和下水道等环境中的高效导航需要优化能源使用.
研究的目的:
- 为自主通道机器人开发一个能源意识的自适应性传感器融合框架.
- 在不影响导航准确性和可靠性的情况下,尽量减少能源消耗.
- 为了在具有挑战性的封闭环境中实现扩展的自主操作.
主要方法:
- 使用RGB摄像头,激光测距器和IMU传感器的自适应式传感器融合框架.
- 适应式扩展卡尔曼波器 (EKF) 用于选择性多传感器数据集成.
- 一个用于动态传感器激活和计算负载调整的能源管理模块.
主要成果:
- 在能源消耗方面实现了35%的降低.
- 观察到对整体导航性能的影响最小.
- 该系统在受限道内的模拟和原型测试中证明了其有效性.
结论:
- 拟议的能源意识的自适应传感器融合框架显著降低了自主通道机器人的功耗.
- 这种方法提高了在管道,下水道和工业管道中长期自主运行的可行性.
- 适应性战略平衡了能源效率与可靠的导航性能.
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