Research on the Development of Key Technologies for the Mechanical

Arm

Haoyi Jin

School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300000, China

Keywords: Mechanical Arm, Artificial Potential Field, Finite Element Analysis, Static Analysis, Image Recognition

Algorithms.

Abstract: The technologies employed in robotic arms also need to be constantly updated and iterated to serve humanity

better. Mechanical arm technology is one of the key research topics today, and researchers are optimizing the

technology for different types of mechanical arms. This article mainly conducts in-depth research and

discussion on four key technologies of industrial robotic arms: precise grasping optimization, flexible

vibration suppression, intelligent obstacle avoidance control, and visual recognition and positioning. The

research results show that the designers have successfully reduced the vibration amplitude of the robotic arm,

improved the grasping accuracy, and shortened the obstacle avoidance response time through innovative

methods such as approximate inertial manifold reduction, finite element structure optimization, joint space

potential field algorithm, and multi-modal sensor fusion. Future researchers can further expand the three-

dimensional visual recognition capabilities and develop adaptive control algorithms to achieve goals such as

multiple robotic arms working collaboratively. This series of research achievements not only promoted the

technological innovation of industrial robotic arms but also provided important technical support for the

upgrading of China's intelligent manufacturing industry.

1 INTRODUCTION

With the continuous advancement of the "Made in

China 2025" strategy, industrial robotic arms, as the

core equipment of intelligent manufacturing, have

witnessed an explosive growth in market demand. In

this context, the intelligent upgrade of the robotic arm

has become a current research hotspot. Among them,

the four key technologies of flexible control, precise

grasping, intelligent obstacle avoidance, and visual

recognition are particularly crucial. Currently,

traditional robotic arms have insufficient capabilities

in adapting to dynamic environments, the vibration of

flexible robotic arms leads to a decrease in

positioning accuracy, the cumbersome grasping

mechanism affects the operational efficiency, and the

two-dimensional visual recognition of robotic arms

limits the application scenarios. These shortcomings

in technology have restricted the development

process of intelligent manufacturing in our country.

This article mainly summarizes and analyzes the

solutions to current problems and the discussions on

https://orcid.org/0009-0002-2871-6679

future challenges and development for four different

types of robotic arms. The research results not only

provide technical support for intelligent

manufacturing. Still, it can also be applied in special

scenarios such as medical assistance and space

operations and has significant engineering

application value and socioeconomic benefits.

2 INDUSTRIAL GRASPING

ROBOTIC ARM

Nanjing Agricultural University has carried out

technical optimization in the field of industrial robotic

arm (Zhu, 2025) grasping. They established a three-

dimensional model of robotic arm using SolidWorks

and conducted static analysis and multi-objective

design optimization for the robotic arm using ANSYS

Workbench. The researchers conducted simulation

experiments focusing on the stress and strain

characteristics of the mechanical claws and the end

effectors under different loads, using the data to

Jin, H.

Research on the Development of Key Technologies for the Mechanical Arm.

DOI: 10.5220/0014323500004718

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2025), pages 145-150

ISBN: 978-989-758-792-4

145

illustrate the durability and reliability of this design

structure (Zhu, 2025). The team also combined the

TRIZ contradiction matrix to optimize the size and

material of the mechanical claw, etc. They used

modeling software to construct the axisymmetric

linkage mechanism model of the robotic arm, and

through symmetrical design, improved the stability

and coordination of the movement. For the

mechanical claw section, the researchers conducted

static analysis to examine the force conditions. They

simulated the stress and strain distribution of the

mechanical components under different loads,

identified the weak links, and reduced the probability

of accidents occurring during the operation process.

During the overall design phase, the team employed

a multi-objective optimization approach, based on the

TRIZ conflict matrix to balance the supporting forces

and create new conflicts, and optimized the size and

material of the mechanical claw, thereby reducing

production costs. For the network division stage, the

designer utilized the adaptive function to generate a

network consisting of 21,048 elements and 99,603

nodes in ANSYS, aiming to balance the calculation

accuracy and efficiency (Gao, Zhu and Tang et al,

2019). The optimized design of industrial grasping

robotic arms effectively addresses several

shortcomings of traditional robotic arms. Through

static stress analysis, the structural reliability of the

mechanical claws under different load conditions has

been verified, ensuring that the maximum stress is

always below the material yield strength and

maintaining a reasonable safety margin. The designer

adopted a design with holes for weight reduction and

a split structure scheme. By reducing the self-weight,

this design utilized the symmetrical linkage

mechanism to maintain the stability of the grasping

process, thereby resolving the contradiction between

weight and structural strength. In terms of cost control,

by selecting cost-effective materials and applying

stress distribution optimization techniques, both

performance requirements were met, and material

consumption was significantly reduced. The entire

design process fully utilized digital tools such as

CAD for virtual simulation, significantly shortening

the iteration cycle of traditional physical prototypes

and achieving simultaneous improvement in design

efficiency and product quality. When this robotic arm

is applied to actual production, it can stably grasp

heavy objects and replace manual labor to complete

repetitive tasks, thereby avoiding unnecessary loss of

manpower. The robotic arm can be remotely

controlled by code programs and can work in high-

risk environments, thereby reducing personnel safety

risks (Arents and Greitans, 2022). Operators can

adjust the grasping angle through the parametric

model, which enables them to handle workpieces of

different sizes and improves efficiency.

The development of future industrial grasping

robotic arms still faces numerous challenges and

opportunities. In terms of hardware, it is necessary to

further enhance the adaptive capability of the end

effector and develop flexible grippers with variable

stiffness to be able to grasp workpieces of different

shapes and materials. Meanwhile, how to reduce the

weight of the robotic arm while ensuring its structural

strength requires more in-depth material research.

Furthermore, issues such as the safety of human-

machine collaboration, the reliability of long-term use,

and cost control all require continuous research. With

the development of Industry 4.0, grasping mechanical

arms needs to be better integrated into the intelligent

manufacturing system and achieve seamless

connection with technologies such as AGV and

digital twins. The resolution of these challenges will

drive the development of industrial grasping robotic

arms towards greater intelligence, greater flexibility,

and greater reliability.

3 RIGID-FLEXIBLE ROBOTIC

ARM

The team from Central South University studied a

model reduction method for flexible-rigid mechanical

arms based on the Approximate Inertial Manifold

(AIM) approach (Piras, Cleghorn and Mills, 2005)

and proposed a feedforward control strategy based on

Particle Swarm Optimization (PSO). The researchers

conducted theoretical modeling, simulation analysis,

and experimental verification, integrating rigid and

flexible arm mechanical systems, and adjusting the

coupling effect of rigidity and flexibility as well as

the nonlinear distributed parameter system, to achieve

efficient reduction of the model order for the robotic

arm and effective suppression of the residual

vibration at the end. The researchers employed an

approximate inertial filtering technique to project the

infinite-dimensional (Xu, Deng and Lin et al, 2022)

flexible-rigid mechanical arm vibration equation into

a finite-dimensional space composed of the

eigenorthogonal decomposition (POD) modes. They

utilized the Galerkin method to simplify the system

(Shi, Yang and Wang, 2023), retaining the

interactions between high-order and low-order modes,

thereby constructing a reduced model. At the same

time, the particle swarm optimization algorithm is

employed to adjust the signal parameters,

EMITI 2025 - International Conference on Engineering Management, Information Technology and Intelligence

146

approximating the input signal as a combination of a

finite number of sine functions. The amplitude is then

adjusted through PSO to minimize the target position

error and end-point vibration. Regarding the sensors

and feedback loops, the designers did not add any

additional equipment. Instead, they directly

suppressed the vibrations by optimizing the input

signals, thus simplifying the control structure. Finally,

the researchers set up a hardware experimental

platform and used resistance strain gauges and half-

bridge circuits to measure the deformation. This

verified the effectiveness of the reduced-order model

and control strategy. The dynamic model of the

flexible-rigid mechanical arm is a nonlinear and

strongly coupled infinite-dimensional system, and

traditional methods are difficult to solve directly for

it. By using the AIM method, the model is explained

as a finite-dimensional one, significantly reducing the

computational complexity. The feedforward control

strategy proposed during the design process

effectively suppressed the vibration by optimizing the

input signal, while avoiding the complexity of sensors

and feedback loops in traditional closed-loop control

(Yavuz, Mıstıkoğlu, and Kapucu, 2012). These

advantages solved the problem of residual vibration

generated by the flexible robotic arm during

movement, which affected the positioning accuracy.

The traditional Galerkin method may lose important

characteristics during modal truncation, while the

AIM method, by retaining the interactions between

high and low order modes, reduces the dimensionality

while maintaining the model accuracy. By effectively

suppressing the end-point vibrations, the positioning

accuracy of the robotic arm has been significantly

improved, enabling it to handle high-precision tasks

such as medical surgeries and precision

manufacturing. Because the feedforward control

strategy has been innovated, there is no need to rely

on additional sensors. This not only reduces the

hardware cost but also simplifies the overall system

architecture. Finally, the researchers adopted a

combined rigid-flexible design for the robotic arm,

which combines the adaptability of traditional

flexible robotic arms with greater load-bearing

capacity. This design enables it to perform better in

special scenarios such as space exploration and

operations in dangerous environments. Meanwhile,

the optimized flexible structure effectively reduces

the motion inertia and energy consumption of the

robotic arm, aligning with the current trend of green

manufacturing. It achieves the dual goals of

performance improvement and energy conservation,

and environmental protection.

In the future, this research can be further

expanded to cover multiple fields. It can integrate

thermal and electrical effects from multiple physical

fields to explore the dynamic characteristics of

flexible and rigid robotic arms in more complex

environments. Researchers can also incorporate deep

learning or reinforcement learning to enhance the

real-time performance and adaptability of the control

strategies, integrate flexible-rigid mechanical arms

into the intelligent manufacturing system, and

achieve more flexible automated production. This

research provides new ideas for the modeling and

control of flexible-rigid mechanical arms. It

demonstrates the theoretical value and practical

significance of the spacing concept. In the future, it is

expected to be widely applied in multiple fields.

4 REAL-TIME OBSTACLE

AVOIDANCE MECHANICAL

ARM

Huazhong University of Science and Technology

mainly studied a real-time obstacle avoidance motion

planning algorithm for industrial robotic arms based

on the improved artificial potential field method

(APF) in joint space (Chen, Chen and Ding et

al,2023). The research subject is a six-degree-of-

freedom robotic arm (such as the ROCRE robotic

arm), and its main objective is to address the

shortcomings of traditional APF methods in terms of

real-time performance, local minimum value issues,

and handling of singular configurations. The research

team verified the effectiveness of the algorithm

through theoretical modeling, simulation comparison

(using Matlab/Simscape), and physical experiments

(Zhang, Wang and Wu,2023). The team adopted the

joint space artificial potential field method, directly

calculating the attractive and repulsive forces in the

joint space, avoiding the need for the inversion of the

Jacobian matrix required by the traditional Cartesian

space APF, and significantly improving the real-time

performance (Chen, Chen and Ding et al,2023). The

researchers adjusted the dynamic learning rate and,

through relevant formulas, dynamically adjusted the

step size of gradient descent to balance the obstacle

avoidance accuracy and speed. This robotic arm

employs a virtual obstacle mechanism. When the

robotic arm gets stuck at a local minimum, virtual

obstacles are automatically generated, enabling it to

break free from the stagnant state without the need for

external input. The researchers also employed the

sphere envelope method to simplify the distance

calculation. They used a sphere to envelop the

complex obstacles, converting the shortest distance

Research on the Development of Key Technologies for the Mechanical Arm

147

from the joint to the obstacle into the distance

between the center of the sphere and the radius,

thereby reducing the computational complexity.

Because the traditional APF requires frequent

calculation of the potential field function or the

inverse of the Jacobian matrix, which is cumbersome,

an improved algorithm is adopted. In each cycle, only

the potential field needs to be calculated once, and the

time consumption is extremely short. Compared with

the Cartesian space APF, it is much faster. The

robotic arm, through the virtual obstacle mechanism,

can autonomously break free from the stagnant state

without human intervention, thus solving the problem

of local minimum traps. Regarding the problem of

singular configuration failure, the direct calculation in

joint space avoids the situation where the Jacobian

matrix becomes non-invertible during the Cartesian

space mapping. When the obstacles are complex and

diverse, the ball envelope method simplifies the

irregular obstacles into a combination of spheres,

which is compatible with the common obstacle types

such as conveyor belts and shelves found in factories.

The improved robotic arm can quickly plan paths in

dynamic environments and meet the high-speed

production demands of the 3C industry. Furthermore,

in terms of hardware costs and safety performance,

this robotic arm can avoid obstacles solely by relying

on the encoder of the base joint, which reduces the

budget and improves the variation of joint angles in

the APF by 62% compared to the genetic algorithm.

The movement is more stable and reduces mechanical

wear and accidental collisions (Chen, Huang and Sun

et al, 2022).

The development of future obstacle-avoiding

robotic arms faces several key challenges. Firstly,

there is the issue of real-time performance in dynamic

obstacle avoidance. The existing algorithms still have

computational delays in complex and changing

environments, and it is necessary to optimize the path

planning algorithm to enhance the response speed.

Secondly, the obstacle avoidance strategy in multi-

obstacle scenarios needs to be improved. Particularly,

the success rate of obstacle avoidance in narrow

spaces needs to be enhanced. In terms of the

perception system, how to reduce the cost of 3D

vision sensors and lidar while maintaining

measurement accuracy is an important issue.

Furthermore, the multi-sensor data fusion technology

still needs to be further developed to enhance the

accuracy of obstacle recognition. In terms of control

algorithms, although AI methods such as deep

reinforcement learning have shown potential, the

stability and generalization ability of these algorithms

still need to be verified. Meanwhile, the energy

optimization of the robotic arm during the obstacle

avoidance process is also a key research direction in

the future. With the development of 5G and edge

computing technologies, cloud-based collaborative

obstacle avoidance systems will become the trend,

which places higher demands on communication

latency and algorithm deployment. The resolution of

these issues will facilitate the wider application of the

obstacle-avoiding robotic arm in fields such as

intelligent manufacturing and warehouse logistics.

5 OBJECT RECOGNITION

MECHANICAL ARM

The Chinese company Yachen Planning and

Designing Co., Ltd. mainly studied a mechanical arm

system based on image recognition and remote

control (Li, 2023). The research adopts a modular

design to divide the system into the remote control

end and the robotic arm end. It uses a camera to

capture the image of the target object, and transmits it

via WiFi to the remote control end for image

processing. After calculating the target coordinates, it

generates control instructions to drive the three-

degree-of-freedom robotic arm to complete the

grasping task (Li ,2023). The system also integrates

ultrasonic distance measurement and temperature

sensors to ensure operational safety and

environmental adaptability. This research employed a

series of key technologies to achieve target

recognition and automatic control: Firstly, using

MATLAB image processing algorithms, the camera

image was subjected to color segmentation and

binarization processing, enabling precise

identification of the target's center position; Then, an

ESP8266 wireless module is utilized to establish a

WiFi connection (Zinkevich, 2021), enabling remote

control and data transmission between devices like

how a mobile phone connects to a router. The entire

system is coordinated and controlled by the Arduino

motherboard as the "brain". It can not only drive the

stepper motor to rotate, but also read the data from the

temperature sensor and ultrasonic distance

measurement device in real time. A mechanical arm

movement calculation formula has been specially

designed, which can convert the screen coordinates

into the motor rotation angle. Finally, a computer

control interface was made using LabVIEW software,

allowing for intuitive data viewing and command

sending. This research effectively addressed several

key challenges in the practical application of

industrial robotic arms: By using WiFi wireless

EMITI 2025 - International Conference on Engineering Management, Information Technology and Intelligence

148

transmission and an efficient MATLAB image

processing algorithm, the delay of control instructions

was reduced to the millisecond level, which was

faster than the common Bluetooth response. It is also

equipped with an ultrasonic distance measurement

sensor and a temperature monitoring module,

enabling the robotic arm to perceive changes in the

surrounding environment just like a human, ensuring

stable operation even in complex conditions. It

replaces the cumbersome manual debugging steps

required by traditional methods with its automatic

target location recognition function, significantly

reducing the operational difficulty. The entire system

is built using affordable Arduino development boards

and ESP8266 communication modules. Coupled with

a flexible modular design, the overall cost is only a

fraction of that of commercial industrial robotic arms.

This makes high-performance automation technology

more accessible and practical. These innovations

have enabled the robotic arm to not only be quick in

response and adaptable, but also easy to operate and

cost-effective, providing a new option for small and

medium-sized enterprises to achieve intelligent

production. The practical benefits brought by this

research are obvious: In the factory, this system can

precisely perform repetitive tasks such as circuit

board grasping and goods sorting， When working in

a high-temperature workshop or a hazardous

environment with radiation, the operator controls the

robotic arm and remotely operates it to perform tasks

from a safe location via WIFI. This research

combines image recognition and wireless control

technologies to provide cost-effective automated

solutions for small and medium-sized industries. Its

core advantage lies in the open-source nature of the

hardware and the lightweight nature of the algorithms

(Bowman, 2023). In the future, through the

integration of three-dimensional perception and

intelligent algorithms.

This research has provided a cost-effective

automated solution for small and medium-sized

industrial scenarios through an innovative

combination of "image recognition and wireless

control”. Its core advantage lies in the open-source

nature of the hardware and the lightweight nature of

the algorithm. In the future, through the integration of

3D perception and intelligent algorithms, it is

expected to further expand its application scenarios.

6 CONCLUSIONS

This study analyzed and discussed four types of

robotic arms. Firstly, based on the reduced-order

modeling method of approximate inertia manifold

(AIM), the ten-order nonlinear system of the flexible

robotic arm was successfully simplified to a three-

dimensional model. While maintaining a certain level

of accuracy, the computational efficiency was

significantly improved, providing a theoretical basis

for real-time control. Secondly, the improved joint

space artificial potential field (APF) algorithm,

through the sphere envelope method and the virtual

obstacle mechanism, reduces the obstacle avoidance

planning time to 0.35 seconds, thereby solving the

failure problem of traditional algorithms in singular

configurations. Thirdly, the lightweight grasping

mechanism design has been verified through finite

element analysis. Under different load conditions,

stress and strain can be monitored promptly, thereby

enhancing the safety factor. Finally, the image

recognition system based on color threshold

segmentation achieved a positioning accuracy of

±1.5mm. Combined with a WiFi remote control, a

complete perception-decision-execution closed loop

is built. These technological innovations have had a

significant impact on the field of industrial

automation. The AIM reduction method and the joint

space APF algorithm provide new ideas for the

modeling and control of complex electromechanical

systems, especially the design paradigm of multi-

sensor fusion architecture, which offers a reference

for the research on the environmental adaptability of

intelligent equipment. Future research can extend to

the study of the ability of perception when it is

confronted with different dimensions. Multiple

sensors or depth cameras can be utilized to overcome

the limitations of a single level. In addition,

researchers can add multiple linked working modules

to the robotic arm, enabling interconnection between

the robotic arms. Through the Internet, they can

control to achieve the goal of collaborative operations,

etc. These future features may drive industrial robotic

arms towards a more intelligent, more flexible, and

more efficient direction.

REFERENCES

Arents, J., & Greitans, M. (2022). Smart industrial robot

control trends, challenges and opportunities within

manufacturing. Applied Sciences, 12(2), 937.

Bowman, R. W. (2023). Improving instrument

reproducibility with open source hardware. Nature

Reviews Methods Primers, 3(1), 27.

Chen, X., Huang, Z., Sun, Y., Zhong, Y., Gu, R., & Bai, L.

(2022). Online on-Road Motion Planning Based on

Hybrid Potential Field Model for Car-Like Robot.

Journal of Intelligent & Robotic Systems, 105(1), 7.

Research on the Development of Key Technologies for the Mechanical Arm

149

Chen, Y., Chen, L., Ding, J., & Liu, Y. (2023). Research on

real-time obstacle avoidance motion planning of

industrial robotic arm based on artificial potential field

method in joint space. Applied Sciences, 13(12), 6973.

Gao, Y., Li, C., Zhu, Y., Tang, J., He, T., & Wang, F. (2019).

Deep adaptive fusion network for high performance

RGBT tracking. In Proceedings of the IEEE/CVF

International Conference on Computer Vision

Workshops (pp. 0–0).

Li, K. (2023). A mechanical arm based on image

recognition and remote control. Journal of Physics:

Conference Series, 2649(1), 012031.

Piras, G., Cleghorn, W. L., & Mills, J. K. (2005). Dynamic

finite-element analysis of a planar high-speed, high-

precision parallel manipulator with flexible links.

Mechanism and Machine Theory, 40(7), 849–862.

Shi, B., Yang, J., & Wang, J. (2023). Forced vibration

analysis of multi-degree-of-freedom nonlinear systems

with the extended Galerkin method. Mechanics of

Advanced Materials and Structures, 30(4), 794–802.

Xu, L., Deng, H., Lin, C., & Zhang, Y. (2021).

Approximate Inertial Manifold ‐ Based Model

Reduction and Vibration Suppression for Rigid ‐

Flexible Mechanical Arms. Complexity, 2021(1),

8290978.

Yavuz, H., Mıstıkoğlu, S., & Kapucu, S. (2012). Hybrid

input shaping to suppress residual vibration of flexible

systems. Journal of Vibration and Control, 18(1), 132–

140.

Zhang, W., Wang, N., & Wu, W. (2023). A hybrid path

planning algorithm considering AUV dynamic

constraints based on improved A* algorithm and APF

algorithm. Ocean Engineering, 285, 115333.

Zhu, Z. (2025). Design and Analysis of an Industrial

Grasping Robot Arm. Advances in Engineering

Technology Research, 13(1), 1012–1012.

Zinkevich, A. V. (2021). ESP8266 Microcontroller

Application in Wireless Synchronization Tasks. 2021

International Conference on Industrial Engineering,

Applications and Manufacturing (ICIEAM) (pp. 670–

674). IEEE.

EMITI 2025 - International Conference on Engineering Management, Information Technology and Intelligence

150