用传感器数据实时识别磁吸爬壁机器人的危险状态
Improved ICNN-LSTM Model Classification Based on Attitude Sensor Data for Hazardous State Assessment of Magnetic Adhesion Climbing Wall Robots
- 融合改进CNN与LSTM的模型分析姿态传感器数据
- 在实验中分类准确率显著优于其他模型
- 适合关注机器人安全监控的研究者和工程师
磁吸附履带式爬壁机器人因能在垂直或倾斜墙面克服重力作业，广泛应用于高空巡检、焊接和清洁任务。但在运行中，自身重量和负载可能产生倾覆力矩，导致磁板脱落并带来安全隐患。本文提出一种基于微机电系统（MEMS）姿态传感器数据的改进ICNN-LSTM网络分类方法，用于实时监测与评估此类机器人的危险状态。首先设计了可捕捉微小振动的数据采集策略；其次提出结合改进卷积神经网络（ICNN）与长短期记忆网络（LSTM）的特征提取与分类模型。实验验证表明，所提微振动感知方法效果显著，且该分类模型在各类测试中均保持高精度，优于其他对比模型。研究成果为爬壁机器人安全运行提供了有效技术支撑。
原文摘要 · Abstract (English)
Magnetic adhesion tracked climbing robots are widely utilized in high-altitude inspection, welding, and cleaning tasks due to their ability to perform various operations against gravity on vertical or inclined walls. However, during operation, the robot may experience overturning torque caused by its own weight and load, which can lead to the detachment of magnetic plates and subsequently pose safety risks. This paper proposes an improved ICNN-LSTM network classification method based on Micro-Electro-Mechanical Systems (MEMS) attitude sensor data for real-time monitoring and assessment of hazardous states in magnetic adhesion tracked climbing robots. Firstly, a data acquisition strategy for attitude sensors capable of capturing minute vibrations is designed. Secondly, a feature extraction and classification model combining an Improved Convolutional Neural Network (ICNN) with a Long Short-Term Memory (LSTM) network is proposed. Experimental validation demonstrates that the proposed minute vibration sensing method achieves significant results, and the proposed classification model consistently exhibits high accuracy compared to other models. The research findings provide effective technical support for the safe operation of climbing robots
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