The lateral line enables fish to efficiently sense the surrounding environment, thus assisting flow-related fish behaviours. Inspired by this phenomenon, varieties of artificial lateral line systems (ALLSs) have been developed and applied to underwater robots. This article focuses on using the pressure sensor arrays based on ALLS-measured hydrodynamic pressure variations (HPVs) for estimating the relative state between two adjacent robotic fish with leader-follower formation. The relative states include the relative oscillating frequency, amplitude, and offset of the upstream robotic fish to the downstream robotic fish, the relative vertical distance, the relative yaw angle, the relative pitch angle, and the relative roll angle between the two adjacent robotic fish. Regression model between the ALLS-measured and the mentioned relative states is investigated, and regression model-based relative state estimation is conducted. Specifically, two criteria are proposed firstly to investigate not only the sensitivity of each pressure sensor to the variations of relative state but also the insufficiency and redundancy of the pressure sensors. And thus the pressure sensors used for regression analysis are determined. Then four typical regression methods, including random forest algorithm, support vector regression, back propagation neural network, and multiple linear regression method are used for establishing regression models between the ALLS-measured HPVs and the relative states. Then regression effects of the four methods are compared and discussed. Finally, random forest-based method, which has the best regression effect, is used to estimate relative yaw angle and oscillating amplitude using the ALLS-measured HPVs and exhibits excellent estimation performance. This work contributes to local relative estimation for a group of underwater robots, which has always been a challenge.


翻译:横向线使鱼类能够高效地感知周围环境, 从而帮助与水流相关的鱼类行为。 受这一现象的启发, 已经开发了各种人工横向线系统( ALLS) 并应用于水下机器人。 文章侧重于使用基于ALLS测量的压感阵列, 根据AllS测量的流体动力压力变异( HPV ) 来估计两个相邻的机器人鱼与前追随者的形成之间的相对状态。 相对的状态包括相对振动频率、 振动以及上游机器人鱼与下游机器人鱼之间的抵消。 受此现象的启发, 各种人工横向线系系统(ALLS) 的品种已经开发并被应用于水下游机器人。 文章侧重于使用基于AllS测量的相对状态之间的反向感应阵列阵列阵列阵列阵列阵列, 具体地说, 不仅调查每个压力传感器对相对状态变化的敏感度, 也包括压力传感器的不充足性和冗余性。 因此, 用于回归分析的压力感应度的传感器是四个典型的回归法,, 包括随机森林的相对的相对回归分析、 相对回归法 和后向后回归法 。 使用 的相对的相对的回归法,, 使用一种 和后向后回归法 的 的 使用 的 的 的 的 的 的 一种对森林的回归法是 的 。

0
下载
关闭预览

相关内容

最新!Yann Lecun 纽约大学Spring2020深度学习课程,附PPT下载
机器学习入门的经验与建议
专知会员服务
92+阅读 · 2019年10月10日
已删除
将门创投
6+阅读 · 2017年7月6日
Arxiv
4+阅读 · 2018年3月14日
VIP会员
相关VIP内容
相关资讯
已删除
将门创投
6+阅读 · 2017年7月6日
Top
微信扫码咨询专知VIP会员