用强化学习动态选特征，提升磁吸附爬壁机器人的危险状态识别能力。
Feature Selection Based on Reinforcement Learning and Hazard State Classification for Magnetic Adhesion Wall-Climbing Robots
- 基于PPO强化学习动态筛选振动特征子集，减少冗余。
- 在复杂工况下分类准确率显著提升，实测效果可靠。
- 适合关注机器人安全监控与智能传感的工程研究人员。
磁吸附履带式爬壁机器人在高空作业中存在倾覆风险，稳定性对安全至关重要。本文提出一种基于近端策略优化（PPO）强化学习的动态特征选择方法，结合典型机器学习模型，旨在提升复杂工况下危险状态的分类精度。首先，创新性地采用基于光纤杆的MEMS姿态传感器采集机器人振动数据，并提取时频域高维特征向量；随后，利用强化学习模型动态选择最优特征子集，降低特征冗余并提升分类性能；最后，采用CNN-LSTM深度学习模型进行分类识别。实验结果表明，该方法显著提升了机器人在多种操作场景下对危险状态的评估能力，为机器人安全监测提供了可靠的技术支持。
原文摘要 · Abstract (English)
Magnetic adhesion tracked wall-climbing robots face potential risks of overturning during high-altitude operations, making their stability crucial for ensuring safety. This study presents a dynamic feature selection method based on Proximal Policy Optimization (PPO) reinforcement learning, combined with typical machine learning models, aimed at improving the classification accuracy of hazardous states under complex operating conditions. Firstly, this work innovatively employs a fiber rod-based MEMS attitude sensor to collect vibration data from the robot and extract high-dimensional feature vectors in both time and frequency domains. Then, a reinforcement learning model is used to dynamically select the optimal feature subset, reducing feature redundancy and enhancing classification accuracy. Finally, a CNN-LSTM deep learning model is employed for classification and recognition. Experimental results demonstrate that the proposed method significantly improves the robot's ability to assess hazardous states across various operational scenarios, providing reliable technical support for robotic safety monitoring.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。