用AI分析机器人操作力数据，自动判断飞机驾驶舱测试是否成功。
Force-Driven Validation for Collaborative Robotics in Automated Avionics Testing
- 通过力矩和姿态数据识别机器人操作，过滤低精度传感器干扰。
- 卷积神经网络准确分类操作成败，并定位失败原因。
- 结合可视化解释技术，提升系统可信度，适合航空检测人员使用。
ARTO 是一个融合协作机器人（cobots）与人工智能（AI）的项目，旨在自动化民用与军用飞机认证中的功能测试流程。本文提出一种深度学习（DL）与可解释人工智能（XAI）相结合的方法，赋予 ARTO 对驾驶舱组件操作进行交互分析的能力，以验证和确认测试过程。在操作过程中，记录力、力矩及末端执行器位姿，并预处理以消除低性能力控系统和嵌入式力矩传感器带来的干扰。采用卷积神经网络（CNN）对机器人操作进行成功/失败分类，同时识别并报告失败原因。为提升可解释性，引入 Grad CAM 这一 XAI 技术，提供模型决策过程的可视化解释。该方法显著提升了自动化测试系统的可靠性与可信度，有助于故障诊断与修复。
原文摘要 · Abstract (English)
ARTO is a project combining collaborative robots (cobots) and Artificial Intelligence (AI) to automate functional test procedures for civilian and military aircraft certification. This paper proposes a Deep Learning (DL) and eXplainable AI (XAI) approach, equipping ARTO with interaction analysis capabilities to verify and validate the operations on cockpit components. During these interactions, forces, torques, and end effector poses are recorded and preprocessed to filter disturbances caused by low performance force controllers and embedded Force Torque Sensors (FTS). Convolutional Neural Networks (CNNs) then classify the cobot actions as Success or Fail, while also identifying and reporting the causes of failure. To improve interpretability, Grad CAM, an XAI technique for visual explanations, is integrated to provide insights into the models decision making process. This approach enhances the reliability and trustworthiness of the automated testing system, facilitating the diagnosis and rectification of errors that may arise during testing.
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。