面向复杂环境的人机协作装配意图识别方法

何家威; 张朝阳; 叶子健; 史天佑

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面向复杂环境的人机协作装配意图识别方法

更新时间：2025-12-01

- 面向复杂环境的人机协作装配意图识别方法
- Assembly Intention Recognition Method in Human-Robot Collaboration for Complex Environments
- 默认刊物名称 2025年
- 作者机构：
  
  1.江南大学机械工程学院,214122
  2.江苏省食品先进制造装备技术重点实验室,214122
  3.江南大学,江苏省无锡市,中国
- 作者简介：
  
  何家威（2001—)，男，硕士生
- 基金信息：
- DOI：
  中图分类号： TH166
- 收稿：2025-04-28，
  
  修回：2025-07-27，
  
  录用：2025-08-29，
- 稿件说明：
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何家威, 张朝阳, 叶子健, 等. 面向复杂环境的人机协作装配意图识别方法[J/OL]. 默认刊物名称, 2025.

HE Jiawei, ZHANG Chaoyang, YE Zijian, et al. Assembly Intention Recognition Method in Human-Robot Collaboration for Complex Environments[J/OL]. Moren Journal, 2025.
何家威, 张朝阳, 叶子健, 等. 面向复杂环境的人机协作装配意图识别方法[J/OL]. 默认刊物名称, 2025. DOI：

HE Jiawei, ZHANG Chaoyang, YE Zijian, et al. Assembly Intention Recognition Method in Human-Robot Collaboration for Complex Environments[J/OL]. Moren Journal, 2025. DOI：

摘要

针对复杂环境中人机协作装配系统识别装配动作准确率较低，并且动作相似度高情况下无法准确感知装配者意图，导致机器人配合操作者的装配效率较低的问题，从装配动作信息和装配零件信息之间的联系分析建立了面向复杂环境的人机协作装配意图识别融合模型。通过基于骨架特征使用一种双流自适应图卷机神经网络（2S-AGCN）模型，针对装配动作进行识别；提出一种改进的YOLOV8模型，以提高网络模型对于装配零件的无序和遮挡环境下装配零件识别的准确率；结合当前装配任务设计了一种包含装配动作和装配零件信息的装配推理规则，规划装配顺序。在装配动作数据集以及装配泵体零件数据集上对所提方法进行了验证。实验结果表明：所提出的改进的YOLOV8模型对于零件识别的mAP50-90的平均准确率达到88.361%；提出的人机协作装配意图识别融合模型对于操作员装配意图识别的准确率达到96.66%，验证了人机协作系统用于识别操作员装配意图的可行性和有效性，将装配动作信息与装配零件信息相结合，有助于在复杂的人机协作环境中准确地识别出操作员的装配意图。

Abstract

Aiming at the problem that the human-robot cooperative assembly system in complex environment has low accuracy in identifying assembly actions

and can not accurately perceive the assembler's intention when the action similarity is high

which leads to low assembly efficiency of robot cooperation with the operator

a recognition fusion model of human-robot collaboration assembly intention in complex environment is established from the relationship between assembly action information and assembly part information. By using a dual stream adaptive graph convolutional neural network (2S-AGCN) model based on skeleton features

assembly actions are recognized; Propose an improved YOLOV8 model to enhance the accuracy of the network model in identifying assembly parts in disordered and occluded environments; A assembly inference rule containing assembly actions and assembly part information was designed based on the current assembly task to plan the assembly sequence. The proposed method was validated on the assembly action dataset and the assembly pump body parts dataset. The experimental results show that the proposed improved YOLOV8 model achieves an average accuracy of 88.361% for mAP50-90 in part recognition; The proposed human-robot collaboration assembly intent recognition fusion model has an accuracy rate of 96.66% for operator assembly intent recognition

which verifies the feasibility and effectiveness of human-robot collaboration system used to identify operator assembly intent. The combination of assembly action information and assembly part information is helpful to accurately identify operator assembly intention in the complex human-robot collaboration environment.

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