International Journal of Research Publication and Reviews, Vol (5), Issue (5), May (2024), Page – 1740-1745

International Journal of Research Publication and Reviews

Journal homepage: www.ijrpr.com ISSN 2582-7421

“AI IN ROBOTICS ADVANCEMENT AND APPLICATIONS”

Kavitha D*1, Meher ujwala N R*2

Under Graduate Students , Department Of Electronics And Communication Engineering.
SJC Institute Of Technology Chickaballapur, Karnataka, India.

ABSTARCT :

AI could be viewed as computing technologies that simulate or imitate intelligent behaviors relevant to the ones of humans despite that they act different from
them . Research areas around AI applications in workplace are related among others to machine learning and deep learning and they can be applied in industries
across the globe. Importantly, AI in the context of job replacement, human-AI collaboration, training, decision making, and recruiting.

Robotics involves the creation of machines that can perform human movement and mimic human behavior. In a nutshell, the field of robotics is a set of sciences
related to artificial intelligence, machine learning, electronics, nanotechnology and many others. The discourse focusing on the developments in the field of
robotic technologies highlights the implications that robots will have on work and employment; whereas at the other end, there is considerable optimism about the
learning and training opportunities that can create for business and people in organizations. Research efforts on robotic technologies can be therefore categorized
in job replacement, human-robot collaboration, and learning opportunities. Research on robotic technologies has predicted that many jobs will soon disappear and
be replaced by automation and robotics.

INTRODUCTION :

Artificial Intelligence (AI) in robotics has revolutionized various industries, from manufacturing to healthcare and beyond. At its core, AI in robotics
enables machines to perceive, learn, and act intelligently in complex environments, often surpassing human capabilities. This synergy of AI and
robotics is driving unprecedented advancements and applications, reshaping the way we work, live, and interact with technology.One of the key areas
where AI has transformed robotics is in perception. Through techniques like computer vision and sensor fusion, robots can interpret and understand
their surroundings with remarkable accuracy. This enables them to navigate dynamic environments, recognize objects, and interact with them
effectively.
Furthermore, AI algorithms empower robots to learn from their experiences and improve their performance over time. Machine learning and deep
learning techniques allow robots to adapt to changing conditions, optimize their actions, and even anticipate future events. This capability is crucial for
tasks that require flexibility and autonomy, such as autonomous vehicles and collaborative robots (cobots).Moreover, AI-powered robotics is driving
innovation in fields like healthcare, where robots are assisting surgeons in intricate procedures, or in agriculture, where autonomous drones are
revolutionizing crop monitoring and management. These applications not only enhance efficiency but also improve safety and quality of life.
 International Journal of Research Publication and Reviews, Vol (5), Issue (5), May (2024), Page – 1740-1745 1741

Figure 1.1: AI for robotics intelligence

Figure1.2 Robotics in healthcare [1]

2. LITERATURE SURVEY

Paper 1
Title : The role of robotics in medical science: Advancements, applications, and future directions
Authors : Arun Agrawal , Rishi Soni, Deepak Gupta, Gaurav Dubey
Published on : 15 January 2024
Description: This paper explores the role of robotics in medical science, focusing on advancements, applications, and future directions. The rapid
evolution of robotics has revolutionized healthcare, particularly in surgical procedures, rehabilitation, and diagnostics. Advancements such as
minimally invasive surgery and robot-assisted surgery have improved surgical outcomes by providing enhanced precision and visualization. Tele-
robotics enables remote surgeries, bringing specialized care to underserved areas. The integration of AI with robotics has led to the development of
intelligent systems capable of analyzing medical data and assisting in decision-making. Robotics finds applications in various domains, including
surgery, rehabilitation, diagnosis, imaging, and prosthetics. The future of robotics in medical science holds promising prospects, including nano
robotics, robotic drug delivery, healthcare automation, and human-robot collaboration.

Paper 2
Title : The Future of Robotics: Advancements and Implications
Authors : Kaledio P, Saleh Mohammed
Published on : 21 Februrary 2024
Description: This paper in the field of robotics has witnessed remarkable advancements in recent years, and its future holds even greater promise.This
abstract provides an overview of the anticipated advancements in robotics and explores their potential implications on various aspects of society.

Paper 3
Title :Artificial intelligence,machine learning and deep learning in advanced robotics
Authors :Mohsen Soori, Behrooz Arezoo, Roza Dastres.
Published on :6 April 2023
Description : Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the field of advanced robotics in recent
years. AI, ML, and DL are transforming the field of advanced robotics, making robots more intelligent, efficient, and adaptable to complex tasks and
environments. Some of the applications of AI, ML, and DL in advanced robotics include autonomous navigation, object recognition and manipulation,
natural language processing, and predictive maintenance. These technologies are also being used in the development of collaborative robots (cobots)
that can work alongside humans and adapt to changing environments and tasks. The AI, ML, and DL can be used in advanced transportation systems in
order to provide safety.

Paper 4
Title : Artificial intelligence, robotics, advanced technologies and human resource management a systematic
 International Journal of Research Publication and Reviews, Vol (5), Issue (5), May (2024), Page – 1740-1745 1742

Authors : Demetris Vrontis , Michael Christofi.

Published on : 12 Feb 2021
Description : This paper aims to systematize the academic understanding of intelligence automation simpact on HRM. It discusses how these
technologies offer new ways to manage employees and enhance firm performance, while also presenting considerable challenges at technological and
ethical levels. The study identifies impacts on HRM strategies (e.g., job replacement, human-robot/AI collaboration) and activities (e.g., recruiting,
training, job performance), providing insights for theory, practice, and future research directions.

Paper 5
Title : Advanced Applications of Industrial Robotics: New Trends and Possibilities.
Authors : AndriusDzedzickis,JurgaSubaciute-Zemaitiene,ErnestasSutinys,UrteSamukaite-Bubnieneand Vytautas Bucinskas.
Published on : 23 December 2021
Description : This paper reviews the advanced applications of robotic technologies in the industrial field. It presents robotic solutions for non-intensive
applications, analyzes their implementations, and provides an overview of survey publications. The analysis reveals obstacles in psychology, human
nature, special AI implementation, and robot-oriented object design.Emerging robot applications face technical and psychological challenges,
suggesting four directions for advancement: development of intelligent companions, improved AI-based solutions, robot-oriented design of objects, and
psychological solutions for robort human collaboration.

Paper 6
Title : Influence of Artificial Intelligence on Robotics Industry.
Authors : Ashok Kumar Reddy Nadikattu.
Published on : January 2021
Description : This paper discusses the profound influence of automation on the robotics industry, emphasizing the integration of Artificial Intelligence
(AI) and robotics. It explores the economic effects of AI and robotics,including self-driving vehicles and machine learning applications. Advantages
and disadvantages of AI are examined, with a focus on its role in enhancing robotics for humanitarian aid and risk mitigation. The paper also outlines
guidelines for AI in robotics, highlighting trends in the manufacturing industry and the future economic impact of AI worldwide.

3.TECHNOLOGIES

Several key technologies are driving advancements in AI for robotics and expanding their applications:
1. Machine Learning (ML): ML algorithms enable robots to learn from data and improve their performance over time without explicit
programming. Techniques such as supervised learning, unsupervised learning, and reinforcement learning are used to train robots for
various tasks.
2. Computer Vision: Computer vision enables robots to perceive and interpret visual information from the environment. This technology is
crucial for tasks like object recognition, scene understanding, navigation, and manipulation.
3. Sensor Fusion: Integrating data from multiple sensors, such as cameras, lidar, radar, and IMUs (Inertial Measurement Units), allows robots
to create a comprehensive understanding of their surroundings. Sensor fusion enhances perception accuracy and robustness, critical for real-
world applications.
4. Natural Language Processing (NLP): NLP empowers robots to understand and generate human language, facilitating communication with
users and other systems. It enables human-robot interaction, enabling robots to receive instructions, ask questions, and provide feedback in
natural language.
5. Reinforcement Learning (RL): RL is a type of ML where agents learn optimal actions by interacting with their environment and receiving
feedback in the form of rewards. RL is well-suited for robotics applications, such as autonomous navigation, grasping, and manipulation,
where trial-and-error learning is essential.
6. Simultaneous Localization and Mapping (SLAM): Techniques enable robots to navigate and map unknown environments autonomously.
7. Human-Robot Collaboration (HRC): Advancements in AI enable safe and intuitive collaboration between humans and robots in shared
workspaces. This involves developing algorithms for motion planning, collision avoidance, and task allocation to ensure efficient and safe
interactions.
8. Robotic Swarms: AI algorithms enable coordination and cooperation among swarms of robots to accomplish tasks collectively. Swarm
robotics leverages principles from biology and social behavior to achieve scalability, fault tolerance, and adaptability.
9. Explainable AI (XAI): XAI techniques aim to make AI models and their decisions understandable to humans. In robotics, XAI enhances
transparency and trustworthiness, enabling users to interpret and validate the actions and decisions of autonomous systems.

These technologies synergistically contribute to the advancement of AI in robotics, unlocking new possibilities for automation, autonomy, and
collaboration in various domains, including manufacturing, healthcare, logistics, agriculture, and exploration.
 International Journal of Research Publication and Reviews, Vol (5), Issue (5), May (2024), Page – 1740-1745 1743

4 .METHODOLOGY

1. Problem Definition and Analysis: The first step is to clearly define the problem or task that the AI-powered robot will address. This
involves understanding the requirements, constraints, and objectives of the application domain, whether it's industrial automation, healthcare,
agriculture, or any other field.

2. Sensor Integration and Perception: AI-driven robots rely on various sensors, such as cameras, LiDAR, radar, and inertial sensors, to
perceive their environment. Integrating these sensors and developing algorithms for perception, including object detection, localization, and
mapping (SLAM), is crucial for enabling the robot to understand its surroundings.

3. Data Collection and Annotation: AI algorithms, particularly those based on machine learning and deep learning, require large amounts of
labeled data for training. This step involves collecting relevant data from real-world environments, annotating it with ground truth labels,
and curating datasets for training and evaluation purposes.

4. Algorithm Development and Training: Once the data is collected, researchers and engineers develop AI algorithms tailored to the specific
robotics task. This may involve traditional machine learning techniques, such as support vector machines or random forests, as well as deep
learning architectures like convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The algorithms are trained using the
annotated data to learn patterns and make predictions.

5. Simulation and Testing: Before deploying AI-powered robots in real-world scenarios, it's essential to validate their performance through
simulation and testing. Simulation environments enable researchers to evaluate algorithms under various conditions, iterate on designs, and
identify potential shortcomings or edge cases. Real-world testing further validates the robustness and reliability of the system.

6. Integration and Hardware Development: AI algorithms need to be integrated into the robotic hardware platform effectively. This
involves developing software interfaces, communication protocols, and control systems to enable seamless interaction between the AI
algorithms and the robot's actuators, sensors, and other components. Hardware considerations, such as power efficiency, computational
resources, and durability, also play a crucial role in the design process.

7. Deployment and Optimization: Once the AI-powered robotic system is developed and tested, it can be deployed in real-world
environments. Continuous monitoring and optimization are essential to ensure optimal performance over time. This may involve fine-tuning
algorithms, updating software, or adapting to changing conditions in the environment.

8. Iterative Improvement and Innovation: The field of AI in robotics is constantly evolving, driven by ongoing research and technological
advancements. Iterative improvement and innovation involve staying abreast of the latest developments, incorporating new techniques and
methodologies, and pushing the boundaries of what's possible in terms of robotic autonomy, intelligence, and functionality.

Benefits of Implementing Robots:-

1. Reduce operating costs

1. 2.Improve product quality and consistency
2. Improve quality of work for employees
3. Increase production output
4. Increase product manufacturing flexibility
5. Reduce material waste and increase yield
6. Comply with safety rules and improve workplace health and safety
7. Reduce labour turnover and difficulty of recruiting workers
8. Reduce capital costs
9. Save space in high-value manufacturing areas.
 International Journal of Research Publication and Reviews, Vol (5), Issue (5), May (2024), Page – 1740-1745 1744

Figure4.1.1 AI-enhanced medical robotics

5 . ADVANTAGES

1.Efficiency and Precision: AI-powered robots can perform tasks with greater efficiency and precision compared to their human counterparts, leading
to improved productivity and quality in various industries such as manufacturing, healthcare, and agriculture.
2. Enhanced Autonomy:AI enables robots to perceive and understand their environment, make decisions, and adapt their actions accordingly, leading
to increased autonomy in tasks ranging from navigation to manipulation.
3.Adaptability:Through machine learning algorithms, robots can learn from experience and adapt to new situations or tasks, making them more
versatile and capable of handling dynamic environments.
4.Safety:AI algorithms can be used to develop advanced sensing and perception systems, allowing robots to detect and avoid obstacles, as well as
collaborate safely with humans in shared workspaces.
5.Cost Reduction:AI-driven automation can help reduce labor costs by replacing repetitive or dangerous tasks traditionally performed by humans with
robotic systems.
6. Innovation Acceleration:AI facilitates rapid prototyping and iteration in robotics, enabling researchers and engineers to explore new ideas and
concepts more efficiently.
7. Personalization and Customization:AI enables robots to analyze data and adapt their behavior or functionality to meet specific user needs, leading
to personalized and customized experiences in fields such as healthcare and customer service.
8. 24/7 Operations :Unlike human workers, AI-driven robots can operate continuously without the need for breaks, leading to increased productivity
and efficiency, especially in industries that require round-the-clock operations such as logistics and manufacturing.
9. Remote Operation and Monitoring:AI can enable remote operation and monitoring of robotic systems, allowing for increased flexibility and
scalability in deployment across various locations or scenarios.
10.Quality Assurance:AI algorithms can be used for quality control purposes, ensuring consistency and precision in manufacturing processes by
identifying defects or deviations from desired standards more effectively than traditional methods.
Human-Robot Collaboration: AI facilitates seamless collaboration between robots and humans in various tasks, leveraging each other's strengths to
achieve optimal outcomes, whether it's in manufacturing, healthcare, or customer service.
12.Predictive Maintenance: By analyzing sensor data and performance metrics, AI can predict potential equipment failures or maintenance needs in
advance, enabling proactive maintenance strategies that minimize downtime and maximize operational efficiency.

6 . CONCLUSION AND FUTURE SCOPE

6.1 CONCLUSION

In conclusion, the integration of Artificial Intelligence (AI) in robotics has sparked a transformative revolution across various industries and sectors.
Through sophisticated algorithms and advanced hardware, AI-powered robots are capable of perceiving, learning, and acting intelligently in complex
environments, surpassing traditional robotic systems in flexibility, autonomy, and adaptability.

The advancements in AI-enabled robotics have led to numerous practical applications, ranging from manufacturing and logistics to healthcare,
agriculture, and beyond. These applications not only enhance productivity and efficiency but also improve safety, quality, and accessibility across
diverse domains.

6.2 FUTURE SCOPE

1. Medical and Healthcare Robotics: AI-powered medical robots will play an increasingly significant role in diagnosis, treatment, and
rehabilitation, revolutionizing healthcare delivery and improving patient outcomes.

2. Ehical and Social Implications: As robots become more integrated into society, there will be a growing need to address ethical and social
implications, including issues related to job displacement, privacy, and robot rights.

3. Robotic Assistants in Education: Robots will be utilized as educational assistants in schools and universities, providing personalized
learning experiences, tutoring, and support to students with diverse needs.

4. Innovative Applications: The convergence of AI and robotics will lead to the emergence of novel applications and industries, creating
opportunities for innovation in areas such as entertainment, sports, art, and fashion.

5. Environmental Sustainability: Robotics and AI will be leveraged to address environmental challenges, such as climate change and
resource conservation, through applications in renewable energy, waste management, and ecosystem monitoring.

6. Neuro-Robotics: Integration of AI with neuroscience will lead to the development of robots that can interact directly with the human brain,
enabling applications such as brain-controlled prosthetics, rehabilitation therapies, and brain-to-brain communication.
 International Journal of Research Publication and Reviews, Vol (5), Issue (5), May (2024), Page – 1740-1745 1745

7. Robots for Extreme Environments: AI-powered robots will be designed to operate in extreme environments such as deep-sea, polar
regions, or outer space, where human presence is challenging or impossible, enabling scientific exploration and resource extraction.

8. Emotionally Intelligent Robots: Advancements in AI will enable robots to perceive, understand, and respond to human emotions more
accurately, enhancing their ability to engage in empathetic interactions and provide emotional support in various contexts.

9. Self-Replicating and Self-Repairing Robots: Future robots may possess the capability to self-replicate and self-repair, leading to
autonomous maintenance and expansion of robotic systems, reducing the need for external human intervention.

REFERENCES :

1. The role of robotics in medical science: Advancements, applications, and future direction ,Journal of Autonomous Intelligence, 15 January
2024,vol 7 ,Issue 3.
2. The Future of Robotics: Advancements and Implications. Kaledio P, Saleh Mohammed,Abubakar Abdulqayyum Ladoke Akintola
University of Technology ,Reasearch gate 21 st Februrary 2024, pp.431
3. Artificial intelligence, machine learning and deep learning in advanced robotics. MohseSoori, Behrooz Arezoo, Roza Dastres.Cognitive
Robotics 6 April 2023, vol 3, pp.54-70.
4. Artificial intelligence, robotics, advanced technologies and human resource management a systematic, Demetris Vrontis , Michael
Christofi,The International Journal of Human Resource Management, 12 Feb 2021,
5. Advanced Applications of Industrial Robotics: New Trends and Possibilities.Andrius Dzedzickis , Jurga Subaciute-Zemaitiene, Ernestas
Sutinys, Urte Samukaite-Bubniene and Vytautas Bucinskas. Applied Science ,23 December 2021,vol 12,pp. 135-145.
6. Influence of Artificial Intelligence on Robotics Industry.AshokKumar Reddy Nadikattu.International Journal of Creative Reseach
Thoughts .January 2021,vol 9,pp.2320-2882.
7. Trustworthy AI and robotics: Implications for the AEC industry,Newsha Emaminejad , Reza Akhavian , Automation in Construction, 3
May 2022, vol 139, pp.104298.
8. Tran, M.Q.; Elsisi, M.; Mahmoud, K.; Liu, M.K.; Lehtonen, M.; Darwish, M.M.F. Experimental Setup for Online Fault Diagnosis of
Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment ,IEEE ,vol 9, Access 2021.pp.115429–
115441.
9. Sughashini, K.R.; Sunanthini, V.; Johnsi, J.; Nagalakshmi, R.; Sudha, R. A pneumatic robot arm for sorting of objects with chromatic
sensor module. Mater. Today Proc. 2021,vol.45, pp.6364–6368.
10. Hespeler S.C. Nemati H. Dehghan Niri E. Non-destructive thermal imaging for object detection via advanced deep learning for robotic
inspection and harvesting of chili peppers. Artif. Intell. Agric. 2021, 5, 102– 117.
11. Rojas, R.A.; Garcia, M.A.R.; Gualtieri, L.; Rauch, E. Combining safety and speed in collaborative assembly systems—An approach to time
optimal trajectories for collaborative robots. Procedia CIRP 2021, vol 97, 308–312.
12. Tankova T.da Silva, L.S. Robotics and Additive Manufacturing in the Construction Industry. Curr. Robot. 2020, vol 1, pp.13–18.
13. Tannous.M, Miraglia.M, Inglese.F, Giorgini.L,Ricciardi.F, Pelliccia.R, Milazzo.M, Stefanini.C.Haptic- based touch detection for
collaborative robots in welding applications. Robot. Comput. Integr. Manuf. 2020, 64,pp 101952.
14. An Investigation of Application of Artificial Intelligence in Robotic, Dinesh Dubey, Udit Kumar Dewangan, Manish Soni, Manish Kumar
Narang,International Research Journal of Engineering and Technology (IRJET) July 2019,Volume 06 ,Issue 07, pp 2395-0072.
15. Kumar, S.; Singhal, P.; Krovi, V.N. Computer-vision-based decision support in surgical robotics. IEEE Des. 2015, vol 32, pp.89–97.