A stacked graph neural network with self-exciting process for robotic cognitive strategy reasoning in proactive human-robot collaborative assembly

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Pengfei Ding , Jie Zhang , Peng Zhang , Youlong Lv , Dexian Wang
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引用次数: 0

Abstract

Proactive human-robot collaborative assembly, a cognitively driven human-robot collaboration, requires research into robot cognitive strategy reasoning to ensure that the robot actively collaborates with the operator in task completion. However, current methods primarily focus on the pairwise relationships of assembly components in discrete snapshots. They could fail to represent the interconnected status of dynamic assembly, leading to inaccurate task allocation, thereby affecting robotic cognitive strategy. To address this problem, we propose a stacked graph neural network (GNN) with self-exciting process to capture the correlation and triggering mechanisms between time-varying tasks. Firstly, a temporal hypergraph with assembly knowledge is constructed to represent the non-pairwise relationships among assembly components in time-varying tasks, aiming to reduce the redundant information brought by pairwise relationships. Then, considering the characteristic of mutual influence between assembly events, a Hawkes process is introduced into the stacked GNN architecture to learn the event correlation representation in the temporal hypergraph. This point process models the self-exciting process of assembly events for simultaneously capturing the individual and collective features of events, thereby revealing the triggering mechanisms of the dynamic events. Finally, the effectiveness of proposed method is demonstrated by comparative experiments and the results of robotic cognitive strategy reasoning on dynamic assembly.
用于主动式人机协作装配中机器人认知策略推理的具有自激过程的堆叠图神经网络
主动式人机协作装配是一种认知驱动的人机协作,需要对机器人认知策略推理进行研究,以确保机器人积极配合操作员完成任务。然而,目前的方法主要关注离散快照中装配组件的配对关系。它们可能无法代表动态装配的相互关联状态,导致任务分配不准确,从而影响机器人的认知策略。为解决这一问题,我们提出了一种具有自激过程的堆叠图神经网络(GNN),以捕捉时变任务之间的相关性和触发机制。首先,我们构建了一个具有装配知识的时间超图来表示时变任务中装配组件之间的非成对关系,旨在减少成对关系带来的冗余信息。然后,考虑到装配事件之间相互影响的特点,在堆叠 GNN 架构中引入霍克斯过程,以学习时序超图中的事件相关性表示。该点过程模拟了装配事件的自激过程,可同时捕捉事件的个体和集体特征,从而揭示动态事件的触发机制。最后,通过对比实验和机器人对动态装配的认知策略推理结果,证明了所提方法的有效性。
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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