SUGISAKAMASANORI

Journal of Robotics, Networking and Artificial
Life Vol. 10(4); March (2024), pp. 353–356
ISSN (Online): 2352-6386; ISSN (Print): 2405-9021
https://alife-robotics.org/jrnal.html

Research Article

Design of Intelligent Handling Robot Based on AT89C52
Fangyv Liu, Jiaxin Wang, Hao He
College of Electronic Information and Automation, Tianjin University of Science and Technology, 300222, China

ARTICLE INFO
Article History
Received 11 November 2023
Accepted 01 July 2024
Keywords
Logistics handling
RobotMCU
Modularization
Obstacle avoidance

ABSTRACTAt present, manual logistics handling is inefficient and limited, Manual handling Most of thecommand and dispatch with experience, it is difficult to carry tasks in real time, confirm thelocation of the handling, cargo inventory and other work tasks time-consuming and laborious,
manual operation is prone to error, workers labor intensity, resulting in greatly reduced work
efficiency. This paper presents a logistics handling robot based on AT89C52 single chip
microcomputer. The integration of the power module, sensor, and drive motor module enables
automatic obstacle avoidance and information collection. Integrating an ultrasonic obstacle
avoidance module alongside an infrared tracking module significantly boosts obstacle evasion
and pathfinding capabilities, adeptly adapting to various work environments. Handling robot arm
structure reference Yaskawa MPL manipulator, the Yaskawa MPL manipulator is highly suitablefor high-speed and high-precision palletizing, picking, packaging, and other industries.
© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

1. Introduction

flexibility. With the continuous development oftechnology, handling robots will play an increasingly
important role in the logistics industry.
However, the current handling robot still has someshortcomings and still faces certain difficulties in thecomplex and dynamic environment. For instance, factors
such as diverse object shapes and sizes, varied terrain
types, and the presence of individuals and other robotic
entities can influence the robot's functionality andnavigation outcomes. The accuracy and flexibility ofhandling robots still need to be improved. This paper
presents a logistics handling robot design centeredaround the AT89C52 single-chip microcomputer. It
enhances current robotic systems by enabling
autonomous tracking, obstacle avoidance, and automatedobject picking using a robotic arm. These advancements
aim to significantly improve the efficiency and safety oflogistics operations.
The rest of this article is organized as follows. The
second part of the design of the robot, first introduces thesystem design of the robot, and then introduces the designof the drive module and the robot arm according to theIn the 21st century, the logistics transportation andhandlingsectorhas
undergonesignificant
transformations due to technological advancements.
While manual handling has a long-standing history inlogistics production lines, it suffers from several
drawbacks including high physical exertion, limited
automation, inefficiency, and environmental impact.
Presently, numerous domestic logistics enterprises
continue to heavily rely on physical handling and manual
operations in their daily logistics processes. Thissituation in the current logistics industry can no longer
adapt to the development needs of the market.
While manual logistics handling remains crucial, itcontends with inherent challenges such as reliance onhuman resources, susceptibility to human errors, reduced
efficiency, and escalating labor expenses. In contrast,
deployment of handling robots offers potential
enhancements in logistics efficiency, cost reduction,
safety enhancement, and heightened adaptability andCorresponding author E-mail: himawari0216@163.com

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system design. The third part is hardware design, which
introduces the main hardware used by the robot. The
fourth part of the module test, mainly ultrasonic obstacle
avoidance module and infrared tracking module fortesting, data analysis. The fifth part summarizes the maincontent of this paper.

The infrared tracking module uses the TCRT5000
infrared sensor as the path detection sensor to realize thedetection of the road track route [2].
2.3. Robot arm design
Mechanical arm structure reference Yaskawa MPL seriesmechanical arm, Yaskawa MPL series mechanical arm
structure is simple, fewer parts, parts of low failure rate,
reliable performance, simple maintenance andmaintenance, less inventory parts required. The MPL
architecture offers exceptional control performance andprecision, addressing intricate motion control
requirements effectively. Its flexible configuration andexpandability cater to diverse application scenarios andspecific operational demands.
The Yaskawa MPL manipulator features a modular
design, allowing for independent installation andreplacement of individual joints and components. This
design simplifies and accelerates repair and maintenanceprocesses, minimizing downtime and repair expenses.
The Yaskawa MPL0100 robot arm model and fixture
model is shown in the Fig. 3.

2. Design of Handling Robot
2.1. Robot system design
The design of this paper is based on the modular designof AT89C52 microcontroller. The functions of the whole
design system are divided into power module, motordrive module, ultrasonic obstacle avoidance module,
infrared tracking module and robot arm module. The
control of the robot arm is achieved through a vision
sensor, through which information about the desired
object is obtained [1]. The System composition diagram
is shown in the Fig. 1.

Fig.1 The system composition diagram
2.2. Drive module design
The whole work flow of the handling robot starts fromthe designated starting work area and gets the routeinformation through the road condition feedback, so as toreach the designated area in accordance with the set route
to complete the corresponding work. Therefore, thedesign of the driving module of the robot in this paper
needs an automatic tracking system to realize. The
automatic tracking system flow is shown in Fig. 2.

Fig.3 Yaskawa MPL0100 robot arm model and fixture
model
3. Hardware Design
3.1. Main control chip
The main controller of the control system uses AT89C52
microcontroller, AT89C52 microcontroller has powerful
functions and flexibility, suitable for a variety ofembedded systems and control applications, belongs to
CMOS8-bit microcontroller. Its advantages are mainlyreflected in low power consumption, high performance,
and flash memory [3], [4]. Receive the input signal
collected by the sensor, and output the control signal toFig.2 The automatic tracking system flow354

Table 1 Ultrasonic obstacle avoidance module test data

the driver chip, so as to control the robot to walk along
the specified route and stop to the specified destination.
3.2. Power supply
In the overall system design, the AT89C52 MCU
functions as the central controller, powered by two 3.7V
power modules. Sensor data acquisition requires a 5V
operating voltage, managed through an AMS1117
voltage regulator. This regulator is known for its costeffectiveness, affordability, stable performance, ease ofuse, low power consumption, overload protection,
multiple output voltages, and high efficiency. The
operating voltage of the motor drive module in the device
is 5 V, if the voltage is greater than 5 V, it is directly
supplied by the power module.

Ultrasonic ranging(mm)Ruler ranging(mm)Error(%)

266.8
350.6
439.5
658.4
772.1

266.0
350.2
439.1
658.0
771.3

0.15
0.18
0.17
0.13
0.14

The error between the ultrasonic obstacle avoidance
module's ranging value and the actual ruler's ranging
value is less than 2%, which can realize automatic
obstacle avoidance.
4.2. Infrared tracking module test
Natural ambient lighting affects the reception capabilityof the infrared receiver, necessitating indoor environment
testing that closely mimics real-world conditions for
accurate evaluation. Before the test, connect P3.2~3.5 ofthe pin J11 and P3.2~3.5 of the AT89C52. Place the robot
on the white test bench, turn on the power, and fine-tune
the potentiometer RW3 and RW4 in a clockwise manner
until LED2 and LED light up.Infrared tracking module
test data is shown in Table 2.

3.3. Drive motor
The robot is usually driven by DC reducer motor, which
can output large torque through the deceleration of thegear box [5]. After thorough evaluation and comparison,
the JGB37-550 DC gear motor model was chosen.
Known for its high torque, excellent stability, wide speed
range, superior energy efficiency, reliability, and ease of
installation, this motor finds extensive applications
across various industrial and commercial sectors.

Table 2 Infrared tracking module test data

3.4. Controls the robotic arm chip
In order to identify the grasping material workpiece, thecolor and shape of the material workpiece are used toidentify the target. The recognition sensor utilizes theOpenMV3 vision module interfaced with an MCU.
Equipped with a camera, the OpenMV3 module captures
images of objects, subsequently processed by theAT89C52 microcontroller.

Transfer robot

Left infrared tube

Right infrared tube

Go forward

11Back up
Turn left

0100

Turn right015. Conclusion
This study focuses on a handling robot, employing theAT89C52 microcontroller as the core processor. It
integrates an infrared tracking module, ultrasonicobstacle avoidance module, power management module,
motor control module, and robotic arm module. This
design aims to enhance operational efficiency andmitigate risks associated with manual logistics handlingin diverse environments.
Intelligent handling robots exhibit promising growth
opportunities. As technology advances and theirapplications diversify, these robots are poised tosignificantly enhance operational efficiency, cut down
expenses, and drive industrial evolution towards greater
intelligence.

4. Module Testing
4.1. Ultrasonic obstacle avoidance module test
According to the actual working environment of thehandling robot, the indoor environment is selected for
ultrasonic obstacle avoidance test, obstacles are set, andECH0 of the HC-SR04 ultrasonic module is connected tothe needle J9-P2.0, TRIG is connected to J9-2.1, VCC isconnected to J9-VCC, and GND is connected to J9-GND.
The LCD1602 liquid crystal was installed in theLCD1602 socket of the single chip microcomputer, andthe contrast of the liquid crystal was adjusted.
Ultrasonic obstacle avoidance module test data is shownin Table 1.
355

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Authors Introduction
Ms. Fangyv Liu

She is currently an undergraduate in
Tianjin University of Science andTechnology. Her research area is about
intelligent robot and intelligent control.

MS. Jiaxin Wang
She is currently an undergraduate in
Tianjin University of Science andTechnology. She is majoring in robotics
engineering.Her research area is about
Image processing based on OpenCV.

MS. Hao He
She is currently an undergraduate in
Tianjin University of Science andTechnology. She is majoring in robotics
engineering.Her research area is about
Image processing based on OpenCV.

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