This article proposes an industrial robot collision detection system based on multi-sensor data fusion and deep learning.By using accelerometers,force sensors,and ultrasonic sensors to obtain robot state and environmental information,Kalman filtering is used to fuse data and improve detection accuracy.Utilizing convolutional neural networks for feature extraction and classification of fused data to achieve high-precision collision detection.The experiment shows that the system is superior to traditional methods in terms of detection accuracy,recall rate,and false alarm rate,and has strong industrial application potential.
关键词
多传感器数据融合/深度学习/工业机器人/碰撞检测系统
Key words
Multi-sensor data fusion/Deep learning/Industrial robot/Collision detection system
维普
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