Article
Technology Recommendations for an Innovative Agricultural
Robot Design Based on Technology Knowledge Graphs
Yucheng Jin
1
, Jizhan Liu
1
, Xiuhong Wang
2
, Pingping Li
3
and Jizhang Wang
1,
*
Citation: Jin, Y.; Liu, J.; Wang, X.; Li,
P.; Wang, J. Technology
Recommendations for an Innovative
Agricultural Robot Design Based on
Technology Knowledge Graphs.
Processes 2021, 9, 1905. https://
doi.org/10.3390/pr9111905
Academic Editors: Arkadiusz Gola,
Izabela Nielsen and Patrik Grznár
Received: 24 August 2021
Accepted: 21 October 2021
Published: 26 October 2021
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Attribution (CC BY) license (https://
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4.0/).
1
Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education,
Jiangsu University, Zhenjiang 212013, China; 1000003026@ujs.edu.cn (Y.J.); liujizhan@ujs.edu.cn (J.L.)
2
Institute of Scientific & Technical Information, Jiangsu University, Zhenjiang 212013, China;
xiuhongwang@ujs.edu.cn
3
College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China; ppli@njfu.edu.cn
* Correspondence: whxh@ujs.edu.cn
Abstract:
The process of agricultural robot design is a complex system requiring the cooperation
and integration of agricultural, machinery, automation, and information technology. These demands
create great challenges for the innovative design of agricultural robots. Meanwhile, more than 95%
of the latest inventions and creations in the world are recorded in the patent literature. In order
to make effective use of the information and data resources of patents, shorten the design cycle,
and provide knowledge for the designers, according to the operation’s objectives, an agricultural
robot technology knowledge graph (TKG) was established for innovative designs. By analyzing
the patent information, a patent IPC co-classification network (IPCNet) for adaptive design process
recognition was put forward to meet the requirements of the different operation objectives and
operation links. Through the extraction of the technology keywords and efficacy keywords, based
on the word co-occurrence network (WCONet), a technology–efficacy map (TEM) was constructed.
Through the integration of the adaptive design process and the TEM, the agricultural robot design
TKG was constructed for determining technological recommendations for agricultural robot design.
The case of the citrus picking robot design was realized to implement the design process. With the
technology recommendation results, the moving system, body, and end-effector for the citrus picking
robot were designed to verify the results of the recommendation.
Keywords: knowledge graph; agricultural robot; technology recommendation; innovative design
1. Introduction
In response to the requirements of smart agriculture and intelligent agricultural equip-
ment, robotic technologies in agriculture are developing rapidly. Compared with traditional
agricultural machinery, agricultural robots have the characteristics of complex structures
and functions, comprehensive electromechanical control, and a complex industrial chain [
1
].
This creates great challenges to R&D for agricultural robots. In the past 30 years, the agri-
cultural robots that have been developed [
2
–
4
] include the complete systems [
5
–
7
] or the
subsystem of a mechanical system (i.e., the manipulator [
8
–
10
] and the end-effector [
11
,
12
],
guidance and navigation [
13
,
14
], and target recognition and localization [
15
–
18
] et al. The
design process for the agricultural robot is the determination of the degrees of freedom
(DOF), the numbers of arms, and the workspace [
19
], and the system design includes the
traveling platform, sensors, manipulations, end-effector, and the control system [2,3,20].
The working environment of agricultural robots is changeable and unstructured.
Therefore, the process of agricultural robot design needs to meet the agronomic demands
of the agricultural operation scene and the operation objective (i.e., the variation of objects
for fruit picking includes the position, size, shape, and reflectance, and the variation of
the sense includes the orchard, greenhouse, indoor, and open field [
19
]), as well as the
cooperation and integration of machinery, automation, and information technology. Those
Processes 2021, 9, 1905. https://doi.org/10.3390/pr9111905 https://www.mdpi.com/journal/processes
