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实时位置和姿势预测,用于一个自动运动的波浪式游泳者在3D空间与人工侧线系统的3D空间
Ruosi Liu1, Yang Ding2,3, Guangming Xie1,4
1State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing, People's Republic of China.
Bioinspiration & biomimetics
|May 9, 2024
概括
这项研究表明,一个人工横线 (ALL) 系统有效地预测了鱼游泳者的位置和姿势,使用神经网络. 发现指导水下机器人鱼相互感知和学校研究.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物启发工程 生物启发工程
- 流体动力学 流体动力学
背景情况:
- 了解鱼类游泳动态对于仿生机器人技术至关重要.
- 人工横线 (ALL) 系统为水下传感提供了潜在的潜水感应.
研究的目的:
- 评估ALL系统的可行性,以便实时预测波浪式游泳者的位置和姿势.
- 分析所有传感器参数对预测准确性的影响.
主要方法:
- 3D计算流体动力学 (CFD) 模拟鱼游泳动力学.
- 在模拟的ALL压力数据上训练人工神经网络 (ANN).
- 评估传感器数量,分布,噪声和采样间隔的影响.
- 开发一个二次ANN来量化预测错误的大小.
主要成果:
- 所有系统,加上ANNs,可以准确地预测游泳者的位置和方向.
- 传感器配置和数据质量显著影响预测性能.
- 开发的本地化网络证明了可靠的错误估计.
结论:
- 人工横线系统是可行的实时水下游泳者定位.
- 这项研究为开发机器人鱼中先进的相互感知和学校行为提供了基础.
- 优化所有传感器阵列是强大的水下机器人应用的关键.
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