Experimental Investigation of Sloshing Force Prediction via Deep Learning and Sensor Fusion in Robotics
Abstract
Accurately predicting the force of water in a moving container remains a challenging task. This paper introduces a novel framework for estimating dynamic sloshing forces in liquid-carrying robotic systems, leveraging a CNN-LSTM model enhanced with an attention mechanism and multi-sensor fusion. A rectangular beaker was mounted on a robotic manipulator, which was equipped with a multi-level water height sensor, a to-axis IMU to monitor beaker motion, and a 3-axis force sensor to capture sloshing-induced forces. The robotic manipulator executed both controlled and random 3D motions with varying velocities and accelerations to induce diverse sloshing dynamics without causing spillage. A sensor fusion algorithm prioritized laser sensor data when ultrasonic readings became unreliable due to high velocities or large sloshing angles. This approach enables real-time sloshing force estimation, laying the foundation for sensor free systems where forces can be accurately predicted.
Mingqi Chen
Postdoctoral Researcher
Qiang Li
Professor and header of AG
My research interests include underwater robot, collaborative robots, humanoid robots.