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A Kalman filter based force-feedback control system for hydrodynamic investigation of unsteady aquatic propulsion

机译:基于卡尔曼滤波器的力反馈控制系统,用于非定常水动力的水动力研究

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Recent advances in understanding fish locomotion with robotic devices have included the use of different robotic prototypes that swim at a controlled but constant swimming speeds. However, the speed of even steadily swimming live fishes is not constant because the fish commonly accelerate and decelerate throughout tail beat cycles. In this paper, we implement a bass body-shaped robot, programmed to display the carangiform fish locomotion. The robotic fish was then mounted on a servo towing system and initially at rest, can determine its self-propelled speed by measuring the external force acting upon it. A Kalman filter was used for filtering the measured external force. By using this method, we tested the speed profiles of both a customized ROV and the robotic fish model. The results show that this experimental method can well predict the speed profiles of both the traditional propeller-based and the undulatory robotic swimmers. In particular, we show that the linear acceleration phase can be reproduced by this experimental method. Finally, we discuss this force-feedback-controlled method and the relative self-propelled hydrodynamic results of the robot.
机译:利用机器人设备了解鱼类运动的最新进展包括使用以受控但恒定的游泳速度游泳的不同机器人原型。但是,即使稳定游动活鱼的速度也不是恒定的,因为鱼通常会在整个拍打周期中加速或减速。在本文中,我们实现了一个低音体形机器人,该机器人被编程为显示carangiform鱼的运动。然后,将机器鱼安装在伺服拖曳系统上,最初处于静止状态时,可以通过测量作用在其上的外力来确定其自推进速度。卡尔曼滤波器用于对测得的外力进行滤波。通过使用这种方法,我们测试了定制ROV和机器人鱼模型的速度曲线。结果表明,该实验方法可以很好地预测传统螺旋桨式和波动机器人游泳者的速度曲线。尤其是,我们表明可以通过这种实验方法来再现线性加速阶段。最后,我们讨论了这种力反馈控制方法以及机器人的相对自推进水动力结果。

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