海洋灵感的多式传感器融合和神经形态处理用于在非结构化的水下环境中实现自主导航
Chandan Sheikder1,2, Weimin Zhang1,2, Xiaopeng Chen1,2
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究引入了一种新的神经形态框架,用于弹性机器人导航,灵感来自海洋物种. 它可以在具有挑战性的水下环境中实现节能,实时路径集成和认知映射.
科学领域:
- 机器人和生物启发的工程机器人学.
- 神经科学和传感器技术
背景情况:
- 在没有GPS的环境中,自主导航面临来自传感器退化和计算效率低下的挑战.
- 现有的机器人系统缺乏动态,上下文感知多感应融合,导致易受故障和高功耗.
研究的目的:
- 开发一种新的神经形态框架,用于在非结构化,无GPS环境中的弹性机器人导航.
- 为了弥合当前机器人系统中存在的动态,上下文感知的多感官融合的差距.
主要方法:
- 共同设计海洋灵感传感器 (复制头足动物神经处理和海洋动物量子机制) 与基于事件的神经形态处理骨干.
- 开发一种混合传感器融合模型,用于实时,节能路径集成和认知映射.
主要成果:
- 拟议的框架允许实时,节能导航,而不依赖传统方法.
- 通过克服最先进解决方案的局限性,证明了提高导航稳健性的潜力.
结论:
- 海洋生物灵感设计为在关键水下应用中推进自主系统提供了有前途的方法.
- 该框架有潜力用于深海勘探,环境监测和水下基础设施检查.
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