An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
复制标题
基于机器学习方法的机器鱼编队环境感知框架
DOI：
10.3390/app9173573
复制
发表时间：
2019
影响因子：
2.7
通讯作者：
Li Chao
中科院分区：
文献类型：
--
作者：
Li Shuman;Yang Wenjing;Xu Liyang;Li Chao
自主式水下机器人（AUV）是近年来机器人领域的研究热点。机器鱼作为一种特殊的水下机器人，可以获得比传统水下机器人更好的推进效率和机动性。研究表明，机器鱼编队比单个机器鱼能节省能量，完成更复杂的任务，但由于附近环境条件难以获取，编队很难保持稳定。受鱼类侧线系统的启发，通过监测鱼体表面的传感器，建立了机器鱼编队的流速预测模型和平台间距判断模型。在计算流体力学（CFD）模拟的基础上，采用多项式拟合和神经网络方法建立了模型。结果表明，该模型预测的流速与实测值的误差可减小到0。4%，间距判断准确率可达80%以上。研究结果对于保持机器鱼编队的稳定性具有重要的指导意义，对机器鱼编队的控制和传感器在机器鱼表面的安装位置具有重要的指导意义。
Autonomous Underwater Vehicle (AUV) has become a hotspot in the field of robot in recent years. As a special kind of AUV, the robotic fish can achieve better propulsion efficiency and maneuverability than traditional AUVs. Studies show that robotic fish formation can save energy and perform more complex tasks than single robotic fish, but it is difficult to maintain a stable formation because the nearby environmental condition is hard to obtain. Inspired by the lateral line system (LLS) of fish, this paper constructs a predictive model of flow velocity and a judgement model of spacing between individual platforms for robotic fish formation through monitoring sensors on robotic fish surface. The models are built by methods of polynomial fitting and neural networks based on Computational Fluid Dynamics (CFD) simulation. The results show that the flow velocity predicted by our model could reduce the error to 0 . 4 % , and the spacing judgement accuracy could reach at least 80%. The findings are useful for maintaining a stable formation and will provide significant guidance for the control of robotic fish formation and sensor installation position on the robotic fish surface.