ACbot: an IIoT platform for industrial robots

doi:10.1007/s11704-024-3449-x

Front. Comput. Sci.

2025, Vol. 19

Issue (4) : 194203 https://doi.org/10.1007/s11704-024-3449-x

Software

ACbot: an IIoT platform for industrial robots

Rui WANG¹, Xudong MOU^1,², Tianyu WO^2,³(

), Mingyang ZHANG⁴, Yuxin LIU¹, Tiejun WANG^1,⁵, Pin LIU⁶, Jihong YAN⁴, Xudong LIU^1,^2,⁵

¹. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
². Zhongguancun Laboratory, Beijing 100194, China
³. School of Software, Beihang University, Beijing 100191, China
⁴. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
⁵. Hangzhou Innovation Institute, Beihang University, Hangzhou 310051, China
⁶. School of Information Engineering, China University of Geosciences, Beijing 100083, China

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Abstract

As the application of Industrial Robots (IRs) scales and related participants increase, the demands for intelligent Operation and Maintenance (O&M) and multi-tenant collaboration rise. Traditional methods could no longer cover the requirements, while the Industrial Internet of Things (IIoT) has been considered a promising solution. However, there’s a lack of IIoT platforms dedicated to IR O&M, including IR maintenance, process optimization, and knowledge sharing. In this context, this paper puts forward the multi-tenant-oriented ACbot platform, which attempts to provide the first holistic IIoT-based solution for O&M of IRs. Based on an information model designed for the IR field, ACbot has implemented an application architecture with resource and microservice management across the cloud and multiple edges. On this basis, we develop four vital applications including real-time monitoring, health management, process optimization, and knowledge graph. We have deployed the ACbot platform in real-world scenarios that contain various participants, types of IRs, and processes. To date, ACbot has been accessed by 10 organizations and managed 60 industrial robots, demonstrating that the platform fulfills our expectations. Furthermore, the application results also showcase its robustness, versatility, and adaptability for developing and hosting intelligent robot applications.

Keywords IIoT platform industrial robots cloud-edge collaboration intelligent applications

Corresponding Author(s): Tianyu WO

About author: Li Liu and Yanqing Liu contributed equally to this work.

Just Accepted Date: 10 January 2024 Issue Date: 23 April 2024

Cite this article:

Rui WANG,Xudong MOU,Tianyu WO, et al. ACbot: an IIoT platform for industrial robots[J]. Front. Comput. Sci., 2025, 19(4): 194203.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-024-3449-x
https://academic.hep.com.cn/fcs/EN/Y2025/V19/I4/194203

Fig.1 ACbot information model. This model design follows the UML class notation with association and inheritance relationships. The black box represents the class for tabular data and the orange box represents the class for time-series data

Fig.2 Functional architecture of ACbot. The colorless modules represent our contribution. The Scheduler serves both in the CoEngine and DeOpEngine

Fig.3 Implementation architecture of ACbot

Tab.1 Communication protocols in ACbot

Fig.4 Architecture of the health condition monitoring model. The left is the model of the 6-axis IR. The blue curves are the third axis’s position, speed, and moment data.

T

is the time window

Fig.5 Pareto front toward welding efficiency, quality, and energy consumption of IR. The quality of the weld is expressed by the Depth-to-Width Ratio (DWR)

Tab.2 Summary of the ACbot platform configuration

Fig.6 ACbot application results. (a) FlowEditor of service orchestration; (b) IR time series; (c) IR monitoring; (d) AGV monitoring; (e) camera monitoring; (f) IR health condition monitoring; (g) IR health condition prediction; (h) Process optimization; (i) IR knowledge graph. (c), (d), and (e) are from the real-time monitoring intelligent application. (f) and (g) belong to the health management intelligent application. In (h), the upper part fits the correlation between the assembly parameters and energy consumption. In the bottom half of (h), the line graph is the correlation fitting deviation between welding parameters and objectives, and the histogram is the importance ranking of the process parameters including Speed (S), Voltage (V), Angle (A), Current (C), Length (L), and Gas flow (G)

Fig.7 Average latency and back-pressure rate of cloud-edge resource scheduling mechanisms

	ACbot	RoboEarth	Rapyuta
Domain	industrial robot	service robot	logistics robot
Tenant	multi	single	single
Architecture	cloud-edge-device	cloud-device	cloud-device
Real-time monitoring	$√$	$√$	$√$
Health management	$√$	–	–
Process optimization	$√$	–	–
Knowledge graph	$√$	–	–
SLAM	–	$√$	$√$

Tab.3 Summary of key features supported by ACbot vs. other platforms

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