基于声信号融合的四肢机器人的地形感知方法
Meng Hong1, Nian Wang1, Xingyu Liu1
1School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究通过将声学数据与自身感知信号相结合,增强了四足机器人的地形感知能力. 这种多式联接方式显著提高了非结构化环境中的移动稳定性和意识,达到高达99.53%的分类准确性.
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
- 传感器融合式传感器
背景情况:
- 四肢机器人运动需要强大的地形感知,以保持稳定.
- 目前使用视觉或自身感知数据的方法与闭塞和模两可的特征作斗争.
- 需要采用多式联网方式来克服单传感器系统的局限性.
研究的目的:
- 为四足机器人开发和评估一种多式地形感知方法.
- 将声学特征与自身感知信号相结合,以提高环境意识.
- 在非结构化环境中提高地形分类的准确性和可靠性.
主要方法:
- 来自外部声音传感器的声学数据采集并使用Mel谱图进行处理.
- 来自惯性测量单元 (IMU) 和联合编码器的自身感知数据被利用.
- 声学和自感特征被连接并输入到支持矢量机 (SVM) 分类器中.
- 优化了SVM超参数和时间窗口长度,以最大限度地提高分类准确度.
主要成果:
- 采用多式联络方法的初始分类准确率达到82.52%,比仅使用自身感应信号的78.28%有所改善.
- 优化SVM超参数和时间窗口长度导致了显著的准确性增加.
- 最后的优化方法在混凝土,石,沙子和地毯地形上实现了99.53%的地形分类准确度.
结论:
- 将声学特征与自身感知信号相结合,可以大大改善四足机器人的地形感知.
- 拟议的多式联运方法提高了机器人的稳定性和复杂地形中的环境意识.
- 优化SVM参数和时间窗口长度对于实现高性能地形分类至关重要.
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