Experimental Investigation of Sloshing Force Prediction via Deep Learning and Sensor Fusion in Robotics

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.