AI 中文总结
针对汽车主动安全测试中移动机器人缺乏自我诊断能力的问题（研究问题），提出基于重建的时间序列异常检测模型（核心方法），能检测多种缺陷类型，验证效果良好，具有强实际可行性（主要贡献）
AI 中文摘要
用于汽车主动安全测试的移动机器人，如超扁平可超越（UFO）机器人平台，目前缺乏检测当前硬件缺陷所需的自我诊断能力，这可能导致更严重故障及高昂维修和停机成本。本文首次为这些移动机器人提出基于重建的时间序列异常检测模型，考虑如全橡胶轮胎磨损不均或减震器损坏等缺陷类别。该方法通过简单预训练步骤利用常规操作中产生的大量未标记数据，优化基于门控循环单元的变分自编码器（GRU-VAE）超参数，评估无状态窗口训练方法和截断反向传播通过时间（TBPTT）方法。通过成功检测六种缺陷类型展示了模型泛化能力，使用从五个系统实例在几个月内不同时间点收集的测试集验证，F1分数达到0.9 36，表明具有很强的实际可行性。
英文摘要
Mobile robots, like the ultra-flat overrunable (UFO) robot platform, used in automotive active safety tests, currently lack self-diagnostic capabilities necessary to detect present hardware defects. This circumstance can lead to more severe failures, causing expensive repairs and operational downtime. This work proposes, for the first time, a reconstructionbased time-series anomaly detection model for these mobile robots, considering defect classes such as unevenly worn full-rubber tires or damaged dampers. Unlike prior publications, the proposed approach leverages the vast quantities of unlabeled data generated during routine operation through a simple pre-training step. Furthermore, it optimizes the hyperparameters of the implemented gated recurrent unit-based variational autoencoder (GRU-VAE) and evaluates both a stateless, windowed training approach and one using truncated backpropagation through time (TBPTT). The model's generalization capabilities are demonstrated by successfully detecting six defect types, with four of them not present in the data used for hyperparameter optimization and threshold selection. This is validated using a test set collected from five system instances at various points over a period of several months, achieving an F1 score of 0.936, indicating strong practical viability.
CommentsAccepted to be published in: IFAC-PapersOnLine, Proceedings of the 24th IFAC World Congress, Busan, Republic of Korea, August 2026
