Simulation Study of Industrial Robots Based on Offline

Programming

Junyu Liu

North Broward Preparatory School, Coconut Creek, Florida, 33073, U.S.A.

Keywords: Automatic Driving, Offline Programming, Matlab and Simulink, Route Planning, Anti-Obstacle Technology.

Abstract: With the wide application of industrial robots in the production field, the manufacturing industry requires

higher productivity and quality, and path planning and simulation technology becomes a critical factor in

improving machine learning. This study focuses on industrial robot path planning and simulation and the

application of Matlab and Simulink in its technology, aiming to improve productivity, reduce cost, and

promote sustainable development. In order to further improve the accuracy of path planning and the efficiency

of robot motion control, machine learning techniques and numerical stability are optimized. For industrial

robot path planning and simulation, Matlab and Simulink simulation techniques are used to study the

kinematic characteristics of the robot and path planning algorithms in depth. By optimizing the offline

programming and path planning algorithms, the efficient operation of the robot in complex environments is

achieved, and the error rate in the production process is reduced. In addition, obstacle avoidance technology

also provides the robot with critical environment sensing capability when performing tasks, further improving

the production line's safety and productivity. Overall, this study provides relevant technical support and

theoretical guidance for applying industrial robots, which helps improve production efficiency, reduce costs,

and realize sustainable development.

1 INTRODUCTION

The digital transformation of the manufacturing

industry has led to an increasing importance of

industrial robots in production. The rapid

development of digital technology has made

manufacturing production more automated and

intelligent. Industrial robots are a key component of

digital production due to their high precision, high

performance, and long working hours. They have

become an important driving force in improving

product quality and production efficiency. Industrial

robots possess high precision operation and

production line automation capabilities (Liu 2022).

They can seamlessly link the accuracy of the

manufacturing process by replacing traditional

manual labor, thereby reducing labor costs.

Additionally, industrial robots excel in handling high

temperatures, high pressures, and hazardous

scenarios, reducing the risk of injuries from human

labor and ensuring the safety of the production

process. The significance of industrial robots is

evident in their ability to rapidly adapt to changes in

production requirements, particularly in offline

programming based on industrial robot simulation

studies (Hu et al 2023).

As mentioned above, the strength of robots lies in

their ability to perform highly repetitive tasks, and

offline programming further improves productivity

by optimizing the execution paths of these tasks.

Within the literature review, several studies have

emphasized the importance of offline programming

in terms of enhancing robot performance and

productivity (Zhao et al 2023). For example, Prof.

Peng Li investigated intelligent perception, decision-

making and sensing systems for robots to improve

their autonomy and adaptability in complex

environments. In addition, Oussama Khatib studies

robot path planning and motion planning, primarily

motion planning algorithms in complex environments

to ensure that robots can perform tasks efficiently and

safely. Finally, path planning plays a vital role in

offline programming. Path planning is to ensure that

robots can efficiently perform tasks in real-world

applications. In-depth Research and improvement of

algorithms can further increase the efficiency of robot

motion, reduce energy consumption, and decrease the

risk of potential collisions. Although the application

Liu, J.

Simulation Study of Industrial Robots Based on Ofﬂine Programming.

DOI: 10.5220/0012837100004547

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

In Proceedings of the 1st International Conference on Data Science and Engineering (ICDSE 2024), pages 537-541

ISBN: 978-989-758-690-3

537

of robotics in the manufacturing industry is gradually

deepening, it is facing problems such as high cost and

long lead time of physical research and development

methods. In order to customer service these

limitations, offline programming techniques have

emerged to provide an innovative way for the

Research and development of industrial robots.

This paper aims to thoroughly investigate the

application of Matlab and Simulink in path planning

and obstacle avoidance techniques to optimize the

motion trajectory and improve the performance of

industrial robots for more efficient automated

production. By overcoming the various limitations of

physical Research, it provides some ways and ideas

for developing industrial robots, which is of great

theoretical and practical significance for improving

production efficiency, reducing costs and realizing

sustainable development.

2 OVERVIEW OF TECHNOLOGY

IN AUTOMATED SYSTEMS

In order to improve the working performance of

robots and automated production efficiency, path

planning and offline programming have become a hot

spot of current Research. Offline programming is the

creation of a three-dimensional simulation of the

robot and computer numerical control (CNC)

machine tools through software simulation

calculations to generate the control robot trajectory,

and then generate the robot's control instructions.

This allows engineers to control robots and CNC

machines in a physical environment (Fu et al 2022).

This technology offers unique advantages to

manufacturing companies and is expected to solve the

problems of high programming costs and long test

cycles. First, offline programming allows the robot to

be programmed independently of the actual

production line, reducing the number of days or even

weeks that engineers must spend in downtime

programming the robot's tutorials. Secondly, offline

programming helps to improve the accuracy and

efficiency of the robot's work. With highly accurate

simulations in a virtual environment, robots can

perform tasks in simulation, allowing for more

accurate workflow planning. It helps reduce the

production process's error rate and improve product

quality. Path planning can significantly improve the

efficiency of the robot (Li et al 2022). By accurately

calculating the optimal path in 3D simulation, robots

can complete tasks faster and more resource-

efficiently, increasing the productivity of the entire

production line. In industrial automation, obstacle

avoidance technology, as an important complement to

path planning, enables robots to sense the

surrounding environment and road conditions in real

time while performing path planning through

intelligent algorithms and sensor systems. This helps

robots to operate efficiently in complex environments

and avoid collisions and work interruptions.

The application of path planning and obstacle

avoidance technology is not only limited to the

familiar field of self-driving cars and drones but also

more widely penetrates the field of simulation

control. Path planning, as a core component of an

autonomous driving system, has the task of

simulating various road conditions, traffic situations

and obstacles in a virtual environment and

determining the reliability, safety and efficiency of an

autonomous driving system in an actual road

environment. Along with path planning, obstacle

avoidance technology has evolved and become

integral to system development (Zhang 2007 & Xu

2021). The system realizes high-precision sensing of

the surrounding environment by integrating multiple

sensors, such as LIDAR and cameras. Despite

significant progress in these technologies, several

challenges remain, including model accuracy and

high sensor costs. In the face of these problems,

optimizing system architectures and algorithms

becomes a key way to reduce cost and improve

performance.

3 TECHNOLOGY

APPLICATIONS IN

AUTONOMOUS DRIVING

SYSTEMS

In autonomous driving systems, path planning aims

to help vehicles find the optimal path in the road

network to ensure safe, efficient, and traffic-

compliant driving. In real-world driving, this involves

comprehensively considering factors such as road

conditions, traffic flow, constraints, and target

locations. To validate the path planning algorithms

for self-driving vehicles, researchers have placed

them into a virtual environment for simulation. This

virtual approach provides researchers with a safe and

controlled test scenario that allows them to evaluate

the performance of the system in different complex

scenarios. By simulating various possible scenarios,

path planning algorithms can be validated and

improved in simulation, thus improving the

ICDSE 2024 - International Conference on Data Science and Engineering

538

feasibility of the system in real road environments

(Zhang 2006).

Another key aspect is the companion technology

to path planning, namely obstacle avoidance.

Obstacle avoidance technology is crucial in automatic

driving systems, and its core lies in realizing high-

precision perception of the surrounding environment

by integrating multiple sensors (Zhou and Li 2018).

Light detection and ranging (LiDAR), as one of the

key technologies, provides the system with accurate

and reliable environmental data and key information

for vehicle navigation safety and obstacle avoidance.

The distance information of surrounding objects is

obtained by emitting laser beams and measuring their

bounce time to construct an accurate 3D map for the

system. This method continuously updates the map as

the vehicle travels, allowing the system to promptly

be aware of static and dynamic obstacles on the road

so that it can adjust its path planning strategy.

Meanwhile, computer vision algorithms analyze the

content of the color images captured by the camera to

provide a further understanding of the environment.

However, despite the significant progress these

techniques have brought, there are still some

challenges. The first is modeling accuracy.

Simulation environments must be able to accurately

simulate a variety of weather conditions, road types,

and driver behaviors in reality. For example,

inclement weather, such as rain or snow, may affect

the performance of the sensors under different

weather conditions, thereby affecting the accuracy of

path planning. The next issue is cost. The high cost of

sensors limits the wide application of these

technologies. To solve this problem, in addition to

optimizing the system architecture and algorithms,

reducing the cost of sensor manufacturing has

become an important research direction. By

streamlining system components, improving the

efficiency of algorithms, and promoting the

innovation of sensor technologies, the manufacturing

cost of the overall system can be effectively reduced,

thus making these technologies more feasible and

sustainable.

4 MATLAB IN INDUSTRIAL

ROBOT PATH PLANNING AND

SIMULATION OF KEY

TECHNOLOGY AND

APPLICATION

Regarding path planning, introducing machine

learning technology provides the system with

stronger learning and adaptation capabilities (Li et al

2022). By learning from many driving scenarios, the

machine learning algorithms can better adapt to

unknown or dynamic environments, which in turn

accelerates the path-planning process. This deep

learning approach is trained in a virtual environment

to continuously improve the system's adaptability to

complex situations and enhance its real-time

performance on real roads.

Optimization in digital stability involves the

accuracy of sensor data and the real-time performance

of the system. Sensor technologies are continuously

improved to increase the accuracy and frequency of

data acquisition.For example, LIDAR technology is

improved to obtain denser point cloud data to

improve accurate perception of the environment (Hu

et al 2022). It is also crucial for computer vision

algorithms to be optimized to speed up image

processing and ensure that the system can make

accurate decisions promptly. Multi-sensor fusion

technology is also a research hotspot, while utilizing

different types of sensors, the system is able to obtain

more comprehensive and multi-dimensional

environmental information, improve the accurate

perception of the road situation, and reduce the

dependence on a single sensor, thus enhancing the

robustness of the whole system. Optimization in

terms of digital stability has already been introduced

with some key techniques. The text will explore two

applications to optimize the simulation speed and

numerical stability further.

Matlab is a powerful visual programming

language. The simulation of robotic work in the

Matlab environment is a critical and complex task, in

particular the construction of a 3D simulation model

of robot kinematics through the Matlab Toolbox

V9.10, which allows not only forward and inverse

kinematics simulation, but also trajectory planning

(Lu et al 2017). The core objective of this work is to

gain an in-depth understanding of the changing rules

of the angles, velocities and accelerations of the

robot's various joints, to provide experimental

analysis means and theoretical support for the later

development and Research of the robot.

The Simulink toolbox became integral to this

simulation process because it allows users to run

simulations to model system behavior and perform

parameter tuning and optimization. However, slow

simulation speed and numerical stability issues are

common problems in Matlab simulations. To counter

these problems, one can consider reducing the

complexity of the model, including simplifying the

model structure to reduce the number of nodes or

layers, removing unnecessary features and

Simulation Study of Industrial Robots Based on Ofﬂine Programming

539

parameters, and reducing the overall computational

complexity. Another approach uses a more efficient

numerical integration method. More stable than

display methods are implicit methods, but they may

also increase computational cost. Alternatively, the

use of adaptive step control or higher order numerical

methods can improve simulation efficiency while

maintaining accuracy. When hardware acceleration is

considered, graphics processing units (GPUs) are

often suitable for parallel computation, especially for

large-scale matrix operations. Transferring

computational tasks in simulation to GPUs can take

full advantage of their large number of parallel

processing units, thus accelerating the entire

simulation process.

Matlab simulation plays an important role in

industrial robotics applications (Zhang 2007). By

building robot models in the simulation environment,

researchers can simulate and analyze the performance

of robots in various work scenarios. This helps

optimize the robot's kinematic performance and

provides a reference for task planning in industrial

production. Especially in the field of industrial

automation, robots are increasingly used in assembly

lines, production plants, and other environments.

Forward and inverse kinematics simulation of

industrial robots is a key link in Matlab simulation.

By simulating the kinematic characteristics of

robots, researchers can gain insight into the

movement patterns of each joint when the robot is

performing a task (Lu et al 2017).This is essential to

improve industrial robots' accuracy, efficiency, and

safety. Positive kinematics simulation allows the end-

effector position to be calculated based on a given

joint angle, whereas inverse kinematics simulation

allows the determination of the desired joint angle to

achieve the desired end-effector attitude. Trajectory

planning is another key aspect in industrial robot

simulation.

Trajectory planning through Matlab allows the

researcher to design the robot's motion path in the

workspace. This requires an in-depth understanding

of the robot's positive and negative kinematic

characteristics to ensure that the designed path meets

the specific task requirements. This is especially

important for scenarios in industrial automation

where complex operations and collaboration are

required. Trajectory planning is not only about the

efficiency of the robot's motion, but also about the

overall effectiveness of the production line.

In Matlab simulation, Simulink's parallel

simulation technique provides an effective way to

increase simulation speed. By dividing the robot

model into multiple parts, the parts can be run in

parallel, thus reducing the overall simulation time

(Xu 2021). This is especially important for dealing

with large-scale robotic systems and complex work

scenarios. Overall, the application of Matlab

simulation in the field of industrial robotics provides

researchers with powerful tools to help better

understand the kinematic properties of robotic

systems, optimize performance, and improve

productivity in real-world applications.

5 GROWING TREND

Automation technology is rapidly evolving with the

advancement of technology. In the future, automation

technology is moving towards integration of robotics,

machine learning algorithms and many other areas.

The current machine learning techniques mainly

concern image recognition, natural language and

speech processing. This allows computer systems to

mimic and understand human perception and

language (Zhang 2006). As the amount of data

increases and algorithms are optimized, machine

learning systems are able to learn more

comprehensively from different domains, covering

both structured data such as sensor data and statistical

information, and unstructured data such as text,

video, and images.

This comprehensive learning acquires more

contextual information for machine learning, which is

expected to give it a deeper understanding of the

nature and complexity of the task. For example,

industrial robots are robots with autonomous decision

making. Not only can they improve manufacturing

efficiency and precision working on production lines,

but they are also able to perform tasks in different

complex environments that are not just limited to

predefined programs. In the future, such autonomous

robots will be utilized in various fields of application.

In the industrial sector, autonomous robots can

flexibly adapt and adjust to the changes brought about

by the production environment, thus further

improving productivity and quality. In the service

sector, home service robots can help the elderly to

live at home, provide learning assistance to students,

answer questions, and even participate in certain

teaching activities. When performing dangerous tasks

such as earthquakes and fires, robots are able to enter

narrow spaces and high radiation areas to perform

search and rescue missions without the physical

limitations of human beings, thus reducing potential

injuries to human beings.

In addition, the widespread use of IoT technology

is a key direction in developing automation. IoT

ICDSE 2024 - International Conference on Data Science and Engineering

540

interconnects various devices in a smart home

system, such as lighting systems, smart thermostats,

and so on. Real-time data is collected to analyze the

occupants' living habits, preferences and daily

behaviors. It provides a more personalized and

comfortable living experience by adjusting the room

temperature, lighting and other environmental

parameters in advance for the occupants. Lastly, IoT

enables efficient connectivity and collaboration

between transportation systems, energy management,

and other facilities in the city. For example,

intelligent transportation systems optimize the

control of traffic lights by obtaining real-time data on

pedestrians and vehicles, slowing down congestion,

improving traffic flow, and analyzing data to predict

where and when traffic jams are likely to occur and to

take measures in advance.

In summary, technology continues to advance.

From comprehensive machine learning to robots with

autonomous decision-making, to the widespread use

of IoT technology in homes, industries and cities,

automation will continue to create a smarter, more

comfortable future for us.

6 CONCLUSION

This paper aims to explore ways to improve

productivity, reduce costs, and achieve sustainable

development through in-depth Research on the

application of Matlab and Simulink in industrial robot

path planning and simulation, as well as the

application of the technology in autopilot systems.

The research methodology mainly includes the

application of machine learning techniques in path

planning, the optimization of numerical stability, and

the key technology and application of Matlab

simulation in industrial robotics.

This paper concludes that offline programming is

crucial for improving robot performance and work

efficiency, especially when dealing with highly

repetitive tasks, and productivity can be further

improved by optimizing the execution path.

Secondly, introducing machine learning techniques

provides the system with stronger learning and

adaptation capabilities, and by training in virtual

environments, the system is better able to adapt to

unknown or dynamic environments, thus accelerating

the path planning process. In addition, optimizing

digital stability, including sensor data accuracy and

system real-time improvement, is crucial to ensure

efficient robot operation in complex environments.

In terms of Matlab simulation technology, a 3D

simulation model of robot kinematics was

constructed using the Simulink toolbox, and forward

and inverse kinematics simulation and trajectory

planning were utilized to gain insights into the motion

laws of each joint of the robot. However, the

simulation process faces slow speed and numerical

stability problems, and the simulation speed can be

effectively improved by simplifying the model

structure, adopting efficient numerical integration

method and using GPU for hardware acceleration.

Overall, the research in this paper covers

industrial robot path planning, simulation technology,

and key technology applications in autonomous

driving systems. The importance of offline

programming, machine learning, and digital stability

is emphasized, while Matlab simulation technology

provides a powerful tool to gain insight into the

kinematic characteristics of robots. As technology

advances, automation technology will see broader

developments in areas such as robotics integration

and machine learning algorithms. By applying more

comprehensive machine learning, autonomous

decision-making robots and IoT technologies, future

automation will be better adapted to the needs of

different fields, improve efficiency, optimize

production and living environments, and create a

brighter, more comfortable future.

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