基于多模态时间序列建模的机器人安全监控

针对多模态时间序列建模的机器人安全监控问题,基于多模态粘性层级狄利克雷过程隐马尔可夫模型（MD-sHDPHMM）提出了一种能准确、可靠、快速、通用地实现机器人执行过程识别与异常事件监测的方法.该方法首先把机器人的执行任务分割成序列化的执行过程,然后通过对比执行过程中累积观察数据的对数似然函数值的大小实现执行过程的识别. 在此基础上,根据正常执行过程训练得到的对数似然函数值的梯度阈值,实现了机器人执行过程中的实时异常监测. 实验结果表明,该方法能有效地实现基于多模态时间序列建模的机器人安全监控.

Multimodal time series modeling-based robot safety surveillance

In this paper, considering the problem of multimodal time series modeling based robot safety surveillance, we present a fast, robust, and versatile measure for executing process identification and anomaly detection through Hierarchical Dirichlet Process Hidden Markov Model (HDPHMM). To effectively improve the robot safety surveillance, first the complex manipulation task into sequences of executing processes was decomposed and then the process identification could be achieved by comparing the log-likelihood value of cumulative observations during robot manipulation. After the process identification, the anomaly detection of each process could also be implemented by discriminating anomalies by the gradient of log-likelihood thresholding from the normal training executions.