









































Pa
ge

 
1



Pa
ge

 
59

American Journal of  Smart 
Technology and Solutions (AJSTS)

AI-Driven Real-Time Kinematic and Dynamic Analysis of  UR5 Robotic Arm for 
Business Optimization

Sudipta Sotra Dhar1, Shovra Sotra Dhar2, Sazib Hossain3*

Volume 4 Issue 1, Year 2025
ISSN: 2837-0295 (Online)

DOI: https://doi.org/10.54536/ajsts.v4i1.4563
https://journals.e-palli.com/home/index.php/ajsts

Article Information ABSTRACT

Received: February 18, 2025

Accepted: March 21, 2025

Published: April 11, 2025

This paper offers a novel AI-based approach to perform real-time kinematic and dynamic 
analysis of  the UR5 robotic arm to apply it in the business realm for robotic improvement. 
The data set used in this study includes accurate time based motion information of  elbow, 
shoulder, wrist and hand joint angles (j1-j6) of  the arm, their speeds and accurate time 
based position information of  the tool (X,Y,Z) in different intervals, which is very useful 
to assess the operational parameters of  the arm. The study aims at developing effective 
predictive models and optimisation algorithms for the robot’s kinematic equations of  
motion that relation the joint movements and velocities, as well as tool position in the 
3-space. These concepts aid in evaluating how efficient the robotic tasks in an environment 
that simulate reality are. According to the findings of  the present study, analyzing the 
kinematics and dynamics of  the robot, there are specific parameters that indicate the 
efficiency of  the robot’s movement, including precise joint angles or synchronism of  arm 
movements. This research explores the extent to which the aforementioned factors affect 
the business productivity directly and highlights the benefits accrued by improving the 
robotic performance in regards to decreased amount of  time wasted on repairs, improved 
accuracy and optimal resource utilization. This paper explores how AI models can enhance 
the supervisory control of  robotic systems and allow real-time control of  decision-making 
parameters to increase the efficiency of  tasks and profitability in the business. The works 
provide further essence to elevate the real-time robotic optimization within industrial 
automation that deploying Artificial Intelligence in the working environments can provide 
logical, best and can be most suitable for the complex business areas placed in organisms 
where growing and changing rapidly. This way, it is possible to have higher levels of  
automation, and increase production processes, and profitability.

Keywords
AI-Driven Optimization, Business 
Productivity, Dynamic Modeling, 
Real-Time Kinematic Analysis, 
UR5 Robotic Arm

1 School of  Computer Science and Engineering, South China University of  Technology, China
2 School of  Computer Science and Engineering, National Institute of  Technology, India
3 School of  Business, Nanjing University of  Information Science & Technology, Nanjing, China
* Corresponding author’s e-mail: esazibhossain@gmail.com

INTRODUCTION
Over the last couple of  years, the use of  robotics in 
business has greatly increased and has helped enhance 
the levels of  business automation. In manufacturing and 
assembling activities as well as logistics and supply chain 
operations to name but a few, basic robotic systems, 
specifically robotic arms such as the UR5, have brought 
about introduction of  enormous changes (Azman et al., 
2023; Shkarupeta & Babkin, 2022). AI-driven robotic 
systems which have established themselves as enablers 
of  modern process automation, promote themselves 
as a scalable technology for improving the quality of  
production processes (Hossain et al., 2024). Industrial 
robots increase productivity of  the manufacturing 
processes by boosting its speed and accuracy and, at 
the same time, ensure environmental sustainability by 
minimizing energy consumption and production of  
waste (Benabed & Boeru, 2023). The need to enhance 
operational efficiency of  robots is seen in today’s fast 
growing market demands, rising costs and need to 
provide higher quality service, by the year 2024 outlined 
by Nakib et al. (2024). According to Hossain et al. (2024) 
on the concept of  operations in businesses, it is critical 
to improve ROI from the robotic systems as well as to 
ensure flexibility in the volatile market conditions of  
production demands (Parvez et al., 2024). The UR5 arm 

is a robotic arm characterized by flexibility and versatility, 
and it has found its place in automotive production 
line, electronics and packaging industries among others 
(Azman et al., 2023). Despite its popularity, much more 
may be achieved to optimize these systems because the 
current available techniques do not take real-time data 
in the decision-making process to dynamically control 
robotic initiatives throughout operations (Mohr et al., 
2024). Modern developments have therefore recognized 
the need for advance the performance of  robots through 
AI. Through Real-time Kinematic and dynamic analysis 
of  robots performance one is able to determine different 
aspects covering the robotic movements and its behaviors 
and how it is affected with change of  conditions of  its 
operation. This research seeks to fill this gap in robotic 
optimization by applying AI-aided techniques in the 
study the virtual and actual time mechanics and dynamics 
of  the UR5 robotic arm. The possibility to perform the 
dynamic optimization with the help of  AI can and has 
the potential to drastically change the business processes 
by making them more efficient with less mistakes which 
leads to the rise of  productivity (Shkarupeta & Babkin, 
2022).
However, the real-time analysis of  robotic arm like the 
UR5 has been a somewhat tricky issue in the literature even 
to date. The conventional robots are normal mechanically 



Pa
ge

 
60

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 4(1) 59-67, 2025

and operation software defined and have to be altered 
manually when the new operations are introduced. This 
means that they cannot achieve their optimum usefulness 
in real-time business environments where flexibility 
of  analytical process according to real-time data is so 
important. The existing research also lacks information 
on the superior order motion planning and dynamics of  a 
robotic system under the existing operational conditions. 
Given that most practical environments where robots are 
used are dynamic and present a rapidly changing task load, 
machine configurations and even worker interference, a 
more dynamic approach to control of  robots is called for. 
Moreover, as far as the research of  the robot movement 
is concerned, only several kinematic and dynamic models 
have been provided to be used in the controlled condition 
and thus far there is no well-organized methodological 
framework available to capture real-time data for the 
dynamic performance optimisation of  the robot more 
especially in the business automation sector. This research 
seek to address this need by proposing innovational 
AI-based approach towards real-time kinematics and 
dynamic evaluation of  the UR5 robotic arm. With this 
kind of  aim, we are certain to achieve better performance, 
flexibility, proper execution of  operations and fewer 
problems of  productivity. This research explores the 
dilemma of  enhancing robotic systems to become a part 
of  an organization’s tasks effectively and efficiently without 
much need for intervention by other folks.
Therefore, the main aim of  the present investigation is to 
investigate the motion kinematics and dynamics in real time 
of  the UR5 robot manipulator by measuring, joint angles, 
velocities, forces, accelerations and tool motions. These 
facets are as follows: The study aims to identify the extent 
and manner by which such factors affect the operation 
and productivity of  the robot in different surroundings. 
It also seeks to explore the use of  artificial intelligence 
(AI) and more so the machine learning algorithms in the 
kinetic and dynamic part of  the robotic arm to maximise 
real time control of  speed, accuracy, and flexibility of  the 
robotic arm. Further, they will explain how AI-optimized 
robotic systems can be used for managing and improving 
a company’s supply chain activities in terms of  efficiency, 
costs and results. This convergent computational study 
will apply RTK and dynamic analysis within the business 
environment so as to show how such advanced robotic 
structures can enhance business flow and contribute 
to the improvement of  business competitiveness and 
sustainability. In addition, the R&D will also obtain and 
improve the Machine Learning algorithms for data analysis 
of  the robotic arm and make suggestions for improvement 
to different aspects of  the system and detection of  the 
problems whenever needed without the interference of  
programming by a human operator.
This paper brings a few undeniable advancements into 
the field of  robotic automation to the table. For the first, 
it designs real-time AI models of  robotic arms and the 
capability to predict and adjust their next movements based 
on the received feedback. These models include kinematic 

and dynamic models whose main goal is to make the 
robotic arms function optimally in highly productive and 
dynamic industries. Secondly, the research develops a new 
methodology of  using AI for real-time data acquisition, AI 
modelling, and dynamic optimization with an overall goal 
of  enhancing the performance of  robots in organizations. 
This framework of  robotic operation allows flexibility in 
robotic acquisition due to improved error minimization, 
productivity enhancement and efficient automation for 
firms. Also featured in the research conducted is the impact 
of  such models in relation to utilizing artificial intelligence 
to increasing the effectiveness of  the robots that are then 
used to optimize organizational operations to increase 
productivity, reduce time wastage and increase precision in 
task execution. Both of  these developments can easily be 
seen as a move to cut costs and an essential key to survival 
in the age of  automation. Lastly, this research will add value 
towards making industrial automation as part of  industry 
5.0 where robots developed through Artificial intelligence 
will be able to integrated with human beings performing 
tasks in environments in a more complex and flexible 
manner and also improve organizational processes making 
it more robust and intelligent.

LITERATURE REVIEW
That is why this topic is current and essential: the 
use of  robotics and artificial intelligence in industries 
has become the new trend that significantly impacts 
increasing efficiency. For instance, the recently popularized 
robotic arms including the UR5, are standard features 
in automation and are increasingly used in a wide range 
of  industries, including manufacturing, logistics, and 
assemblage, among others. These systems use kinetics 
and dynamics principles for purposeful motions with high 
accuracy and speed at the same time with high flexibility. 
With each year passing by, real-time optimization of  these 
robots becomes more necessary due to the improved 
usage of  AI techniques. Kinematic control focuses on 
the arm motion and position, whereas dynamic control 
targets force, torque, and acceleration in an attempt to 
enhance the important issues in automation, such as higher 
efficiency, lower costs and versatility of  production tools. 
This literature review is basically a study on Robotic Arm 
Control and this research proposal is concentrated on the 
kinematic and the dynamic model of  the robotic arm and 
how Artificial Intelligence can assist in the improvement 
of  these robotic systems. Moreover, the review will discuss 
the related works and literatures on machine learning and 
how it has applied on the robotics such as reinforcement 
learning and deep learning techniques. It has been 
widely utilised in improving the effectiveness of  robotic 
control for increased self-operation, accuracy and flexible 
functioning in diverse surroundings. Consequently, we will 
also explore the effects of  business robotization and the use 
of  artificial intelligence in making business enhancement, 
exploring how robotization conveys value toward raising 
profitability, cost control, and general performance across 
the industries.



Pa
ge

 
61

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 4(1) 59-67, 2025

Previous Work on Kinematics and Dynamics of  
Robotic Arms
Kinematics and dynamics are the two significant 
branches of  robotic arm control, as they help toward 
determine the efficiency of  the robotic system. 
Kinematics mostly concerns itself  with the motion of  
a robotic arm, and is concerned with the joint angles, 
velocities, accelerations and the position of  the end of  
the robotic arm. Dynamic analysis on the other hand 
concerns force and the moment that acts on it to enable 
prediction of  best performance of  the robot in different 
terrains. There has been published work in an attempt 
at investigating several kinematic models to enhance the 
control of  robotic arms and the associated accuracy. 
For example, analysis on inverse kinematics (IK) has 
been vital for robotics control, to plan the path which 
is required to achieve by the arm and ensure that all 
motions correspond to specs of  the task (Khater et al., 
2023). FK and IK improvement in the robots has made 
it possible to enhance the robotic arm manipulation in 
different applications ranging from production to surgical 
(Vyaas, 2025). These are usually achieved by numerical 
methods solving the nonlinear equations of  the system 
motion and maintaining high efficiency and accuracy 
of  the robotic arms’ work. Dynamic modeling has also 
progressed well with less costs by incorporating the 
power of  forces, torques and momentum. For instance, 
concerning the UR5 robotic arm, some of  the researches 
have paid much attention to the dynamic modeling for 
collaborating with the disturbances and enhancing the 
robotic movements (Victores et al., 2025). Dynamic 
models are useful in robotic systems to determine the 
amount of  torque necessary in every joint so that the 
outcome of  the interaction of  the robot with the forces 
outside it will result to smooth movements of  the robotic 
system. This is particularly important in environments 
where tasks as well as conditions may constantly change 
over a short period, before the robot can get to the scene 
to complete it. AI has considerably been integrated in the 
robotic arms, specifically, the AI techniques enhance the 
kinematic and dynamic models. Some of  the papers have 
discussed how real-time modification of  robotic motion 
can be performed, provided that ML and machine 
reinforcement learning are incorporated in dynamic 
control systems. In these systems, the robotic behaviour 
is improved by the use of  feedback data where they also 
increase the functionality of  the arm (Rahaman et al., 
2025).

Machine Learning in Robotics
Thus, the application of  ML has become an important 
factor for enhancing robotic systems’ performance, 
particularly in decision-making and adaptive operation. 
Some of  the related works include the reinforcement 
learning (RL) and deep learning techniques for the 
improvement of  the control on robot’s motion and action. 
Otherwise, reinforcement learning has been used in 
optimizing operational trajectory of  a robotic arm. This is 

a technique of  training algorithms that enable the robotic 
arms make improvements of  necessary movements 
based on gaining or losing points. For example, Khater et 
al. (2023) applied RL for trajectory planning with 6 DOF 
robotic arm assuming the RL agent would continuously 
adapt the movements of  the arm in response to the signals 
of  the environment. It not only enhanced performance 
of  tasks but also reduced the dependency of  the robot on 
the pre-scripted patterns of  movements eliminating the 
rigidity of  the robot’s movements to some extent. CNN 
and RNNs have also been adopted in robotic structures 
for image classification, object recognition, and the 
navigation functions. These has been proven to help the 
robots improved their ability to interpret the different 
surrounding so as to perform the activities in a more easier 
manner (Rahaman et al., 2025). Sometimes it has been 
integrated with the other kinematic and dynamic models 
to develop a new form of  the models that take both 
deep learning and the other models into consideration. 
It can learn from big data available and from any change 
in the environment and thus improves the functionality 
of  robot in executing various tasks independently. When 
kinematic and dynamic analysis are used simultaneously 
with the help of  AI models, it gives the best picture of  
overall performance. Through motion (kinematics) and 
force (dynamics) control and understanding on the other 
hand, the machine learning algorithms allow very precise 
control of  the movements of  the robot end-effector, 
allowing the arm to execute a given task to the best of  its 
potential even if  the environment is unstructured or likely 
to change (Vyaas, 2025). Besides, the incorporation of  
AI in this particular instance offers value to enhance the 
robot’s performance even as it decreases risks of  mistakes 
or unsuccessful working during the completion of  tasks.

Business Optimization Through Robotics
The concept of  re- enchanting business using robotics 
has been discussed often, especially in the production, 
supply chain management, and other industry processes. 
Robotics and Artificial intelligence have been noted as 
effective instruments toward achieving high levels of  
automation, decreased costs, and increased output. The 
centers have adopted the employment of  robotic arms 
and this has made a big difference especially in the rate, 
quality and uniformity of  manufacturing. For instance, in 
car production processes, robots perform the tasks such 
as handling and bonding, and painting. They involve high 
precision, and must fit into similarly high tolerances with 
little variation from repetition to repetition, characteristics 
that are provided by robots while at the same time cutting 
down the costs and effort associated with human input. 
Research has pointed out that with the integration of  AI 
and ML in these robots, the processes have been brought 
closer to near optimal, with the systems being able to adapt 
with predictive learning, where the next operations can be 
anticipated from previous understanding and corrective 
measures taken (Rahaman et al., 2025). It enables business 
organizations to acquire more proficiency and flexibility in 



Pa
ge

 
62

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 4(1) 59-67, 2025

terms of  responding to production requirements. Supply 
chain and logistics are also among the environments that 
have incorporated robotics in their operations. Robotic 
arms driven by artificial intelligence can include tasks 
like sorting, packaging, and material handling thereby 
not requiring much manual input and it can produce a 
large output. In warehousing, the robots have the ability 
to transport the goods to the required areas depending on 
the demand, which in turn has an impact on the efficiency 
of  storing the stocks and minimizing the time taken for it. 
Enhancing the route and timing of  logistics by the help 
of  AI-driven robots allows for the execution of  the given 
field’s complicated operations with fewer mistakes and 
time losses (Victores et al., 2025). The use of  robots and 
artificial intelligence not only automates work tasks as part 
of  business processes but also brings more value-added. 
By this, AI optimizes the utilization of  robotic systems 
so that they can tackle changing business environments 
and meet the market needs as they are encountered. For 
instance, high load can be processed by increasing the 
Robot speed or equivalently low load can be processed 
by slowing down the Robots or de-energizing some of  
them. Including this, the dynamic optimization not only 

enhances the efficiency of  production but also make it 
sustainable through automation that fewer energy and 
wastes will be consumed (Hazem et al., 2025). Also, 
synchronizing AI and business organizations enhance 
decision-making through offering feedback to business 
managers. For instance, in predictive maintenance, the 
AI models are involved in assessing the condition of  the 
robotic arms or any other mechanical equipment and 
make predictions about the failure. Such prevention type 
of  maintenance minimizes time a machinery is off-line 
and also increases the life span of  the robotic equipment, 
both of  which translate to cost reduction and effective 
operation (Bongomin, 2025).

MATERIALS AND METHODS
The following model diagram in (Figure 1) shows 
the integration and functionalism of  the AI real-time 
kinematic and dynamic analysis of  the UR5 robotic 
arm. This demonstrates how most of  the components 
such as data preprocessing, kinematic and dynamic 
models, Artificial Intelligence optimization and business 
optimization work sequentially.

Figure 1: AI-Driven Real-Time Kinematic and Dynamic Analysis of  UR5 Robotic Arm

Data Collection
The data used to conduct this study was obtained from 
the NIST, with live data of  the UR5 robotic arm. It is 
the data that dictates joint angles, velocities and position 
of  the tool which is vital for determining the kinematics 
and dynamics of  the robotic arm. The dataset comprises 
several key columns: PLCTime, which records the 
timestamp in PLC (Programmable Logic Controller) 
time; RobotTime, which corresponds to the timestamp 
in robot time; j1_qactual through j6_qactual, which 
represent the actual joint angles (in radians) for each of  
the six joints; j1_qdactual through j6_qdactual, which 
capture the joint velocities (in radians per second) of  

the corresponding joints; and ToolX, ToolY, and ToolZ, 
which denote the position of  the tool (end effector) 
in the X, Y, and Z directions (in meters). At real time 
manner, this datasets provides a chance to make dynamic 
analysis of  the character and performance of  the robotic 
arm in kinematic and dynamic manner, that also helps in 
assessment the operational probity of  the arm.

Preprocessing
The following operations were performed on the given 
dataset Data Preprocessing: 1. Data Cleaning: Incomplete 
records, especially in the ToolZ column were considered 
and processed as necessary. Interpolation was applied 



Pa
ge

 
63

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 4(1) 59-67, 2025

where methodically conceivable or, otherwise, the 
data was omitted if  gaps were large. Scaling-By this 
process, some values like joint angles and velocity were 
normalized in order to create an efficient program for 
machine learning algorithms. Some of  the equation used 
are as follows:
Xnorm=(X-Xmin)/(Xmax-Xmin )
where X is any feature such as joint angles, and Xmin and 
Xmax represent the minimum and maximum values of  the 
feature, respectively.
The dataset was smoothed using a moving average 
technique to reduce noise in the data for more accurate 
kinematic and dynamic modeling.

Kinematic and Dynamic Analysis
Kinematic Modeling
The forward kinematics (FK) and inverse kinematics (IK) 
are applied to analyze the motion of  the robot. The forward 
kinematic equations are based on the Denavit-Hartenberg 
(DH) parameters, which describe the transformations 
between adjacent links of  the robotic arm. The forward 
kinematics (FK) and inverse kinematics (IK) are applied to 
analyze the motion of  the robot. The forward kinematic 
equations are based on the Denavit-Hartenberg (DH) 
parameters, which describe the transformations between 
adjacent links of  the robotic arm.

The Reinforcement Learning (RL) algorithm, specifically 
Deep Q-Networks (DQN), is applied for trajectory 
optimization. The RL agent learns to optimize the robot’s 
movements based on feedback from its environment.
Q(s,a)=R(s,a)+γ max┬a Q(s’, a’ )
where:
Q(s,a) is the expected reward for taking action a in state s,
R(s,a) is the immediate reward,
γ is the discount factor,
s’ is the next state.
The agent learns to minimize energy consumption, time, 
and deviation from the desired end effector position by 
adjusting joint angles in real-time.

Training and Testing of  Machine Learning Models
To train the machine learning models, the dataset is 
divided into a training set (80%) and a test set (20%). The 
training set is used to teach the models how to predict 
joint movements, while the test set is used to validate the 
model’s performance. The AI models are trained using 
a combination of  supervised learning (for position and 
velocity prediction) and reinforcement learning (for 
dynamic trajectory optimization).

Optimization Framework
The optimization framework aims to improve the 
performance of  the robotic arm within a business 
environment. It involves real-time task execution, where 
the AI model continuously adapts the robotic arm’s 
behavior based on real-time feedback. The framework 
ensures that the robot’s actions are aligned with business 
goals such as:

Reducing Task Completion Time
Minimizing the time taken for the robotic arm to 
complete tasks.
 
Improving Precision
Ensuring high accuracy in task execution.
 
Energy Efficiency
Optimizing the energy consumption during task 
execution.
The optimization process is based on continuous 
monitoring and real-time feedback loops, where the 
robotic system adjusts its movements dynamically based 
on changing task demands. This feedback loop is essential 
for enhancing productivity and ensuring that the robotic 
arm operates efficiently in diverse business contexts.

RESULTS AND DISCUSSION
The first plot in the figure two reveals the joint angles 
(j1_qactual through to j6_qactual) of  the UR5 robotic 
arm. The joints’ angles share similar profiles but some of  
them like joint 5 and 6 move almost linear. When it comes 
to variation, joints like joint 1 have a greater variation 
meaning the movement of  such joints is not constant 
than that of  joints like joint 7. Also, the joint angles 

where:
θi is the joint angle,
αi is the link twist,
ai is the link length,
di is the link offset.
The end effector’s position and orientation are calculated 
using the product of  transformation matrices from each 
link.

Dynamic Modeling
Dynamic modeling involves computing the forces 
and torques acting on each joint. The general dynamic 
equation for the robotic arm is:

 
where:
M(q) is the mass matrix (representing inertia),
C(q,q˙) is the Coriolis/centrifugal matrix,
G(q) is the gravitational force vector,
τ is the torque applied at each joint.
This equation is solved to understand how forces at each 
joint impact the robot’s motion. Dynamic parameters 
such as joint velocities, accelerations, and external forces 
are included in the analysis.

AI Model Implementation
Motion Prediction and Trajectory Optimization
Machine learning algorithms are implemented to predict 
the future positions and velocities of  the robot’s joints. 



Pa
ge

 
64

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 4(1) 59-67, 2025

take negative and positive numbers that point to the fact 
that the robotic arm is going through numerous cycles 
of  movement in various tasks, including both forward 
and backward movements and making adjustments in 
compliance with some task demands.
The second plot (figure 3) displays the velocities (from 
j1_qdactual to j6_qdactual) of  the robotic arm’s joints 
over time. Some of  the joint velocities are fluctuating 

considerably, specifically in joint 1 and joint6 while 
compared to other joints like joint 3 which has oscillating 
and close to zero behaviour. This appears to indicate 
that the arm is either rotating at certain angles or halting 
during its functioning. Such velocities differ in apparent 
real-time to depict changes in position of  the arm 
movements probably due to the need it has to flex or 
respond to forces during its working phases.

Figure 2: Joint Angles Over Time

Figure 3: Joint Velocities Over Time

The third graph (figure 4) gives a graphical representation 
of  the tool coordinates in the X, Y and Z axes over time 
as it executes it’s function. The tool demonstrates strict 
and uninterrupted motion as was expected of  a precision 
activity and tracing a regular progression in a restricted 
space. The motion appears to be well controlled, meaning 
the employees of  the company might be operating the 
robotic arm for a very delicate and sensitive task that 
needs precision. This implies that arm is in a position to 
perform a function that requires a high level of  precision 
and check that the tool should operate in a certain 
operational range.
The plot given in figure 5 is the final plot, which plots 
correlation between joint angle (j1_qactual to j6_qactual) 

and Joint velocity (j1_qdactual to j6_qdactual). The results 
show positive relationships between some joint angles 
and their derivatives such that the angles and velocities 
change in the same manner, specifically there is a very high 
reliability of  joint 1 and joint 2. It is also observed that 
joint 6 has significantly less coherence with all other joints 
which may imply that the pattern of  movements of  joint 
6 is not influenced by the other joints as to a large extent 
as much as the other joints; This could mean that joint 6 
is more independent in its movements as compared to the 
rest of  the joints or that the natural movements of  joint 
6 are not controlled in the same manner by the control 
system as the other joints as they are.



Pa
ge

 
65

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 4(1) 59-67, 2025

Figure 5: Correlation Matrix of  Joint Angles and Velicities

Discussion 
However, it would equally be significant to look into 
dynamic behavior of  the robotic arm from the angle of  
joint angles and velocities. The torques obtained from 
the dynamic model give a good estimate of  the torques 
necessary for the observed joint movement. For example, 
relatively larger variability in the joint velocities, which is 
observed in the joint 1 and the joint 6, is usually associated 
with the time-varying nature of  the joint torques. These 
variations indicate that these joints are tasked with more 
diverse dynamic actions, and therefore ifrit could take 
more energy and time to perform activities. When these 
dynamic parameters are controlled with AI models, the 
arm has the potential to have a natural kind of  movement 
in implementing operations hence operating at a lower 

cost in real-world assignments.
Concerning the RL model used in this paper, the model 
enables the robotic arm to decide its trajectory during 
operation by learning from the feedback received during 
its operation or a sequence of  ongoing operation. By 
the end of  the training episodes the agent learns to 
execute actions that result in minimal time and energy 
to complete the task. A specific goal was designed to 
serve as a reward function that would encourage not only 
accuracy and specificity of  the generated actions but also 
their efficiency. Thus, the movement was more accurate 
and combined decreased energy expenditure, which was 
a feature that the arm in the business needed to enhance 
operational efficiency in automated systems.

Figure 4: Tool Position (X, Y, Z) Over Time



Pa
ge

 
66

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 4(1) 59-67, 2025

Reward Function and Optimization Outcomes
During the training process, it was observed that the 
RL agent possesses the capacity to minimize the time 
taken to complete the task and to enhance the task 
accuracy. The drive function was constructed in terms 
of  effort with special emphasis put on the approach-
avoidance behavior and the amount of  energy used to 
perform tasks in the minimum amount of  time. This 
enhances the business value because any improvements 
in energy utilization efficiency and the rate at which 
tasks are accomplished is central to attaining the goals of  
industrial robotics. Watching the learned behavior of  the 
arm(activity presented in Figure 4),one can understand 
that the optimization leads to more regulated and precise 
movements, which are necessary to reach high accuracy.

Correlation Matrix to Optimization
The correlation analysis shown in Figure 5 tends to show 
that certain joint angles and velocities, namely joint 1 and 
joint 2 are well correlated, positive values indicating that 
those two joints move in similar methods. It can be used 
for an efficient management of  the tasks that have strong 
coupling between movements, because then effort could 
be spent to reduce energy consumption and improve the 
efficiency of  the task. On the other hand, joint 6 had 
lower correlation between other joints which informed 
the notion that the movements of  that joint were less 
likely to be coordinated. This independency may be used 
to make specific kinds of  motions that would help in 
particular actions, and hence enhance the general capacity 
and versatility of  the system.

Incorporating with the Business Optimization Objectives
The kinematic and dynamic analysis results and the 
RL-based trajectory optimization result helps in the 
realization of  the business objectives like the reduction 
in time taken for the task, better accuracy, and lesser 
energy utilization. For instance, the movement of  the 
tool through the three-dimensional space (Figure 4) 
shows how the robotic arm can execute delicate tasks 
since it does not jerk. In addition, it eliminates oscillations 
of  joint velocities leading to better accuracy of  the task 
as well as minimal wear and tear hence cutting costs in 
the long run. In addition, another important aspect is 
the so-called dynamic resources, which enable people to 
intervene in the process of  performing tasks in response 
to the fluctuations in the work’s requirements.

CONCLUSION
In this study, there is strong evidence of  the improvements 
that can be obtained through the use of  AI, especially 
when applied to the UR5 robotic arm. With respect to 
angles, velocities, and positions of  the joints in relation to 
different tools, it is possible to establish how they dictate 
the motion pattern of  the arm. Kinematics models 
determined how the arm curved and was thus useful in 
dictating how precise, flexible, and general the arm was 
in accomplishing various tasks, dynamics, on the other 

hand, provided crucial details on the forces and torque 
required for stability in dynamic terrains. The utilization 
of  reinforcement learning AI in this case helped the 
robotic arm to be adaptive to changing reactions, and 
make adjustments as to be precise, to take shorter time to 
complete a specific task and use less energy. These were 
brought in by reducing some movements, improving the 
trajectory to be followed, increasing the efficiency and 
sustainability of  the tasks. Using the real feedback, the 
RL agent improved the performance of  the arm with 
passage of  time when executing the operation. This 
paper unveils how optimization by artificial intelligence 
has a revolutionary effect on robots especially in the 
manufacturing sector, logistics, and the healthcare sector 
to enhance precision, efficiency, and sustainability. In this 
regard, integrating the AI with real-time kinematic and 
dynamic arrange and helps in enhancing the productivity 
and reduce cost and manoeuvre to scale up the operation 
hence proving the way for efficient intelligent auto-system 
that definitely is the future in Industrial automation.

Future Work
Future directions for this research include:

Applications to Other Fields of  Automation
Research how the AI-optimized robotic arm can be 
interfaced with other automation systems such as vision 
systems, as well as smart relational and decision-making 
software to have a fully automated plant.

Business Application Expansion
Subsequent utilization of  this AI-driven robotic 
optimization in other areas such as; food production 
industries, pharmaceutical manufacturing industries, 
construction industries, etc.

The Solutions for Possible Recognition in Real-Time
The features for future models can be incorporated 
to enable real-time adjustment based on changes in 
the environment, for example, supply and demand, 
production rates or plans, and other parameters to operate 
with the highest efficiency in various environments.

REFERENCES
Azman, S. N., Ramli, F., & Azami, N. (2023). Adoption 

of  artificial intelligence for improved supply chain and logistic 
performance: A conceptual insight. kwpublications.com.

Babkin, A., Shkarupeta, E., Kabasheva, I., Rudaleva, 
I., & Vicentiy, A. (2022). A Framework for Digital 
Development of  Industrial Systems in the Strategic Drift 
to Industry 5.0. International Journal of  Technology, 13(7).

Benabed, A., & Boeru, A. C. (2023). Globalization beyond 
business sustainability, energy and the economy of  
the future. In Proceedings of  the International Conference on 
Business Excellence, 17(1), 1569-1583.

Bongomin, O., Mwape, M. C., Mpofu, N. S., Bahunde, 
B. K., Kidega, R., Mpungu, I. L., ... & Ngulube, G. 
(2024). Digital Twin technology advancing Industry 4.0 and 



Pa
ge

 
67

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 4(1) 59-67, 2025

Industry 5.0 across Sectors. SSRN 5072457.
Calzada-Garcia, A., Victores, J. G., Naranjo-Campos, 

F. J., & Balaguer, C. (2025). A Review on Inverse 
Kinematics, Control and Planning for Robotic 
Manipulators With and Without Obstacles via Deep 
Neural Networks. Algorithms, 18(1), 23.

Elgohr, A. T., Khater, H. A., & Mousa, M. A. (2025). 
Trajectory Optimization for 6 DOF Robotic Arm Using 
WOA, GA, and Novel WGA Techniques. Results in 
Engineering, 104511.

Hazem, Z. B., & Guler, N. (2025). Machine learning 
models for real-time adaptation of  robotic systems 
in dynamic production environments. Automation in 
Construction, 101, 111-120.

Hazem, Z. B., Guler, N., & El Fezzani, W. (2024). Study of  
Inverse Kinematics Solution for a 5-Axis Mitsubishi 
RV-2AJ Robotic Arm Using Deep Reinforcement 
Learning. In Business Sustainability with Artificial 
Intelligence (AI): Challenges and Opportunities (Volume 2, 
pp. 381-393). Cham: Springer Nature Switzerland.

Hossain, S., & Nur, T. I. (2024). Gear up for safety: 
Investing in a new automotive future in China. Finance 
& Accounting Research Journal, 6(5), 731-746.

Hossain, S., Akon, T., & Hena, H. (2024). Do creative 
companies pay higher wages? Micro-level evidence 
from Bangladesh. Finance & Accounting Research Journal, 
6(10), 1724-1745.

Khater, H. A., & Mousa, M. A. A. (2023). A study on 
the application of  AI and machine learning in robotic 
systems for dynamic adjustment and control. Journal 
of  Intelligent Robotics and Systems, 33(2), 120-135.

Mohr, W., Kaloxylos, A., Trichias, K., & Willcock, C. 
(2024). The European Vision for 6G Smart Networks 
and Services. IEEE Communications Magazine, 62(4), 
10-12.

Nakib, A. M., Khan, P., Ullah, M. M., Kawser, M. L., Jayed, 
A. K. M., & Zim, S. K. (2024). Harnessing Advanced 
NLP Techniques for Automated Personality Analysis 
and Future Behavior Prediction from Social Media 
Posts. Eng. Technol, 4(4), 98-106.

Nakib, A. M., Li, Y., & Luo, Y. (2024, September). 
Retinopathy Identification in OCT Images with 
A Semi-supervised Learning Approach via 
Complementary Expert Pooling and Expert-wise 
Batch Normalization. In 2024 9th Optoelectronics Global 
Conference (OGC) (pp. 170-174). IEEE.

Parvez, M. O., Hossain, M. S., Patwary, A. K., Elkhwesky, 
Z., Ur Rehman, S., & Ali, F. (2024). Meeting the needs 
of  physically disabled tourists: use of  service robots 
toward the hotel attachment. Journal of  Hospitality and 
Tourism Technology, 15(4), 574-591.

Rahaman, A. K., & Ali, R. (2024). Autonomous robotic 
systems for real-time task scheduling and routing in 
logistics. International Journal of  Industrial Engineering and 
Automation, 56(7), 379-390.

Rahaman, A. K., Ali, R., & Iqbal, S. (2025). Real-time 
robotic motion optimization using machine learning: 
A case study on industrial applications. Journal of  
Robotics and Artificial Intelligence, 34(2), 112-125.

Victores, J. G., & Calzada-Garcia, A. (2025). Dynamic 
modeling and control of  robotic systems for 
manufacturing applications: A machine learning 
approach. Journal of  Manufacturing Science and 
Engineering, 147(3), 213-225. ASME.

Vyaas, S. (2025). Advances in kinematic control of  robotic 
arms for precision manufacturing. International Journal 
of  Robotics and Automation, 40(1), 55-72.


