Anomaly Detection for Dynamic Human-Robot Assembly

被引：3

作者：

Schirmer, Fabian ^{[1
]}

Kranz, Philipp ^{[1
]}

Schmitt, Jan ^{[1
]}

Kaupp, Tobias ^{[1
]}

机构：

[1] Tech Univ Appl Sci, Inst Digital Engn, Wiirzburg Schweinfurt, Germany

来源：

COMPANION OF THE ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION, HRI 2023 | 2023年

关键词：

Human robot collaboration; human action recognition; LSTM-based autoencoder; dynamic assembly sequence;

D O I：

10.1145/3568294.3580208

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Human action recognition is one of the key challenges in humanrobot collaboration (HRC), especially when the process has multiple valid ways to assemble a product. To address this problem, we developed an anomaly detection framework for the assembly of complex products. We used an Long-Short-Term-Memory (LSTM)-based autoencoder to detect anomalies in human behavior and post-process the output to categorize it as a green or red anomaly. A green anomaly represents a deviation from the intended order but a valid assembly sequence. A red anomaly represents an invalid sequence. In both cases, the worker is guided to complete the assembly process.

引用

页码：881 / 883

页数：3