ARTIFICIAL INTELLIGENCE IN AUTONOMOUS SYSTEMS
AND ROBOTICS
Harshan Kumar RG, Gautham Mourya, Kaviarasan M
Department of Computer Science and Engineering, Faculty of Engineering and Technology,
SRM Institute of Science and Technology Tiruchirappalli,
Tamil Nadu-621105, India.
ABSTRACT
This paper discusses the use of Artificial Intelligence(AI) in Autonomous, robots and systems
with emphasis on important technologies including computer vision, deep learning. These
technologies have allowed robots to accomplish intricate tasks such as adaptive decision
making, object recognition and dynamic navigation. Case studies emphasize the use of such
technologies in autonomous vehicle, industrial automation and health care robotics. The
research also discusses challenges that includes safety data and ethics. Experimental outcome
proves enhanced accuracy and efficiency with AI based models. This study is intended to
shed light on creating intelligent, reliable, adaptive robotics system for various industries
Keywords: Artificial Intelligence (AI), Autonomous Robots, Autonomous Systems, Computer
Vision, Deep Learning, Adaptive Decision-Making, Object Recognition, Dynamic Navigation,
Case Studies, Autonomous Vehicles, Industrial Automation, Healthcare Robotics, Safety
Data, Ethics,
Experimental Outcomes, Enhanced Accuracy, Enhanced Efficiency, AI-Based Models,
Intelligent Robotics Systems, Reliable Systems, Adaptive Robotics, Various Industries
INTRODUCTION
Artificial intelligence in autonomous technology is a robotics technology that has entirely
reshaped the possibilities and horizons of innovation. Equipping machines with the ability to
sense, process, and act autonomously, AI has revolutionized industries by solving some of the
most confounding problems and record-smashing innovations in transportation, healthcare,
agriculture, and space exploration. These technologies are a testament and say a lot about the
accuracy and effectiveness of autonomous systems as great and precise solutions to dynamic-
causing problems posed within any application domain. Modern autonomous systems employ
AI technologies like machine learning, deep learning, and computer vision. Machine learning
is more suitable for robots since it allows them to learn and improve over time. Deep learning
methods enable robots to examine enormous amounts of information and can detect intricate
patterns. Computer vision allows robots to perceive their surroundings and make decisions in
real-time. The technologies allow autonomous systems to do more than they are normally
requested to do, even in unstructured and unpredictable environments, like autonomous cars
to navigate complex traffic patterns and drone robots to map inaccessible areas. But with the
benefits of rapidity of bringing AI to autonomous systems come demonic issues to address up
front. On or near top of the list of problems to overcome to make safe facilitation of ingestion
of the technology an option are system dependability, ethics, and robot AI capability interface
safety. In addition to this, AI in robots also puts in the forefront transparency, accountability,
and social implications of more and more advanced and autonomous machines. The purpose
of this research paper is to examine the situation of AI in robots and autonomous machines,
in the context of new advancements, their capacity to revolutionize, their issue and
implications of current existence, and their influence in the future. It is earnestly hoped that
through the research, the paper will be in a position to add to what is understood on AI-based
technology towards developing self-sustaining efficient systems for inspecting. With the
coming of AI and autonomous systems, robotics has seen phenomenal change as it opened up
new paths to machine ability. AI enabled autonomous systems to sense, decide, and move on
their own. This experienced mythical innovation in transport, medicine, agriculture, and
space travel. Technology like machine learning, deep learning, and computer vision makes it
possible for robots to learn from uncertain and dynamic environments. They convert sensory
data, comprehend dynamic worlds, and execute tasks with efficiency and precision. From
self-driving cars navigating city roads to robot rovers on extraterrestrial planetary surfaces,
uses transform industries and resolve complex issues. However, certain problems remain in
merging AI with autonomous systems. To make systems stable, resolve ethical concerns, and
make AI-driven robots harmless are the most research-critical goals to attain. In this paper,
the context at hand, capacity revolution, and social impacts in the context of AI in robots and
autonomous systems are analysed.
LITERATURE SURVEY
1. Pandemic of COVID-19 and the Role of Artificial Intelligence and Robotics in
Healthcare has accelerated the integration of robotics and artificial intelligence in
healthcare. Intelligent robot systems have found wide applications in diagnosis, risk
assessment, monitoring, telehealth care, disinfection, etc. By doing so, they
significantly reduce the burden on the first-line workers, enhance the efficiency of
health care services, etc. In addition, the COVID-19 pandemic has become an
incentive for the further development of AI-powered tools, which allowed to
significantly speed up the process of the COVID-19 vaccine discovery and
distribution. Research has shown that AI and robotics can be used to support mental
health by providing comfort to people suffering from stress and anxiety related to the
COVID-19 pandemic. The literature review systematically compiled and analysed
147 selected articles, which reflect the significant role of AI and robotics in the fight
against the COVID-19 pandemic. It shows the prospects for AI and robotics in health
care and the need for further research in this field.
2. Current advancements in AI and related technologies have enabled substantial
breakthroughs in the autonomous navigation of mobile robots in unstructured settings.
Cameras (RGB), motor encoders, and low-cost IMUs are instrumental in robot
navigation and location. Neural networks and Simultaneous Localization and
Mapping (SLAM) are employed for path and marker detection. Navigation algorithms
autonomously direct robots from location A to location B, negotiating obstacles. This
research places more importance on the growing importance of autonomous robots in
industry where possibilities for automation of repetitive jobs and improving
production are underscored. Future studies must overcome existing technical
challenges in an effort to develop completely independent multi-functional robot
systems.
3. Planning and control of autonomous mobile robots (AMRs) in intralogistics has been
widely researched in recent times. The key focus is given to the efficiency and
reliability enhancement of AMRs under dynamic and complicated scenarios.
Fragapane et al. (2021) conducted a comprehensive review, describing key challenges
and developing a research agenda to resolve these challenges. The research stresses
the importance of efficient navigation algorithms, effective path planning, and real-
time decision-making. The coupling of AI and machine learning techniques has been
observed to enhance the flexibility and effectiveness of AMRs. Additionally, the
authors stress the importance of interoperability and standardized protocols across
robotic systems to enable seamless functioning. This review of the literature indicates
the potential for AMRs to revolutionize intralogistics through material handling
automation, reduced labour costs, and the highest operational effectiveness. Flexible
and scalable solutions would be needed to be created in future research that can
handle intralogistics with evolving needs.
4. Based on what is written on the webpage called "Tensions and antagonistic
interactions of risks and ethics of using robotics and autonomous systems in long-term
care". The utilization of robots and self-governing systems within extended care has
certainly grown into a prominent subject for research, dealing with the increasing
demands from a maturing group of people. It examines at the ethical as well as risk-
related tensions involved with the use by RAS in caregiving settings. The report
highlights the necessity of a balanced approach to weighing the collection of
advantages of RAS, including increased efficiency and decreased caregiver burden,
against a number of risks around privacy issues and ethics concerns. The writers
systematically go over the texts and also locate frequent subjects along with problems
in using RAS. They put great emphasis on the vital necessity of the total creation of
unquestionably sturdy regulatory frameworks along with moral guidelines for
completely guaranteeing accountable usage of these types of technologies in many
extended care locations. This review stresses the importance of a number of active
cross-disciplinary studies. These studies are important in order to solve the detailed
risk-ethics relationship throughout RAS deployment.
5. According to the page "Brains for Robots: Application of the Mivar Expert Systems
for Implementation of Autonomous Intelligent Robots". The progress in the
development of intelligent robots has gone a big way with the creation of Mivar
Expert Systems. the use of the systems in production of autonomous intelligent
robots, which acknowledge their ability to handle big volumes of data efficiently.
Mivar Expert Systems leverage a novel approach to reasoning and knowledge
representation to allow robots to make decisions in real-time. The paper elaborates on
the dominant methodologies and illustrates the merits of Mivar systems compared to
conventional expert systems, such as scalability, flexibility, and implementation ease.
Integration of Big Data methodologies enhances the efficiency of robots further,
making them autonomous to work in complicated and dynamic scenarios. This
literature review points towards the potential of Mivar Expert Systems to transform
robotics as a strong foundation for the development of intelligent, autonomous
machines. Further research has to continue to make efforts to enhance these systems
to address real-time processing and flexibility problems.
6. the capabilities of spiking neural networks (SNNs) in allowing multi-task autonomous
learning for mobile robots. Spiking neural networks, which draw inspiration from the
brain's native neural networks, provide a good solution to solve intricate tasks as they
are energy-efficient and capable of processing information in a biologically plausible
way. The scientists utilized SNNs to upgrade the learning potential of mobile robots,
allowing them to accomplish various tasks autonomously. This paper draws on
existing work within autonomous robotics, where the conventional neural networks
have been widely applied. The authors do point out weaknesses of traditional neural
networks, including their requirement of high computational power and lack of
biological realism. The results of the study show that SNNs can greatly enhance the
performance and efficiency of mobile robots in performing a range of tasks,
proposing a possible revolution in the future design of autonomous robotic systems.
7. The fusion of 5G/6G mobile communications with transport and robotics has been
driven much further, as presented. The evolution of 5G has revolutionized data
transmission rates, latency, and connectivity, enhancing the efficiency and reliability
of communication for ITS. This has enabled the embedding of AI in many areas like
computer vision, natural language processing, and machine learning that are the
mainstays of intelligent manufacturing and autonomous transport. The technological
innovation capabilities of 6G technology with the features of higher end-to-end
connectivity and terahertz communications offer ITS another enhanced capability to
achieve. The survey highlights the importance of edge intelligence and federated
learning in managing security and privacy concerns and improving the performance
of AI models. The paper concludes that synergy between 5G/6G, AI, and robotics will
be a leading force in shaping the future of the intelligent system and industrial and
academic innovation.
8. a complex approach in autonomous robots and robotics through the merging of brain-
computer interfacing (BCI), goal generation flexibility, and autonomous robotics with
deep learning. The article presents the possibility of integrating the above
technologies to create a dynamic service robot that can learn to perform multiple tasks
and respond to the user's needs. Robots that are autonomous, with strong algorithms
and sensors, can move and interact within dynamic environments. Dynamic goal
setting makes it possible for the system to adjust its objectives according to feedback
received in real time and the changing conditions. The deep learning-based BCI
enables the subject to interact with the system based on neural signals, providing an
intuitive and productive method of interaction and control over the robotic device.
The authors refer to the requirement for such interdisciplinary solutions to advance
the purpose of service robots, actually rendering them more practical and ideal for real
deployment. This research contributes to the growing literature focusing on creating
smart and adaptive robotic systems.
9.
10. novel approach to the scheduling of charge for mobile robots, focusing on adaptive
charge prioritization with a reliance on task-dependent data. Conventional strategies
for battery control utilize fixed-time intervals or constants, which manifest as rigid,
suboptimal behaviours. This paper offers an algorithm based on a multi-objective
sequential solution to a Markov decision process (MDP) from the time-dependent
variety. This model allows the robot to learn when important tasks are most probable
so that it can charge during off-peak hours. The scheduler proposed is proven to be
effective through extensive testing with real-world deployment data. The results show
that the adaptive approach substantially enhances the robot's performance by
achieving a trade-off between the need to keep the battery levels above a user-
specified value and maximizing task execution rewards. This study illustrates the
possibility of improving the operation performance and lifetime of autonomous
mobile robots using intelligent battery management techniques.
Proposed Methodology
Methodology for the integration of Artificial Intelligence (AI) in autonomous systems is critical to the
success of resilient, adaptive, and successful robotic solutions. The subsequent section provides the
heterogeneous steps and processes applied in developing and launching AI-based autonomous
systems, all falling under ten related subtopics.
1. Comprehensive Data Gathering and Preprocessing
Data as the Foundation of AI is Success with any stand-alone AI starts with good data collection. Data
is the foundation on which models are trained and is at the heart of building models that can read and
make decisions in adverse environments. Autonomous Vehicle Sensors are GPS modules, LiDAR
sensors, and high-definition cameras provide spatial and visual data for navigation and obstacle
detection. Industrial automation systems collect process data like operation history and quality-control
feedback. Medical Devices such as MRI, CT scan data, and robot surgical equipment provide input to
precision health programs. There will be dirty, inconsistent, and incomplete raw data. Data
preprocessing will have to be used to scrub the data clean of quality and usability for AI algorithms.
Data Cleaning is useful for Identification and repair of data defects, i.e., duplicate or missing records.
Defective pixel images, for instance, are removed or fixed. Normalize numerical data to achieve
consistency. For instance, distance measures in all situations can be translated to meters to prevent
inconsistencies.
Challenges of Data Collection
Autonomous generate terabytes of data that require good storage and handling arrangements.
Weather and lighting could bring variability in the quality of data. Ethics are observed in collecting
data. An example is medical databases where patient data must be anonymized such that the datasets
are regulatory compliant, for example, GDPR.
2. Data Annotation and Quality Control
Importance of Annotation
Data annotation is a tagging process of raw data to render it readable for AI algorithms:
Image Data: Labeling objects (e.g., cars, pedestrians) of images to be employed in computer vision
applications.
Text Data: Phrases or sentence identification to render them readable by natural language processing.
Time-Series Data: Exceptions and trend marking in sequential data such as traffic flow or machine
run.
annotation Techniques
Manual Annotation: The data is annotated by human annotators. It's time- and expense-intensive but
can be depended upon.
Automated Annotation: Machine-pre-tagged data with post-tagging and human monitoring. It may be
pre-calculating object bounding boxes on images, e.g.
Measures for Controlling Quality
Data quality control entails:
Extremely carefully checking for correct annotations.
Using inter-annotator agreement metrics for conflict resolution.
Too much re-tweaking of annotations upon tweaking the set.
3. Model Selection Strategy
Knowledge of the Problem Statement
Choosing the right model is to have exactly a clue about what the problem is. For instance:
Classification Tasks: Object class recognition of objects in images or text.
Regression Tasks: Prediction of numerical output like distance from an obstacle.
Reinforcement Learning: Teaching robots what's the best decision in a noisy environment.
Model Types
Convolutional Neural Networks (CNNs): For image tasks such as object detection and semantic
segmentation.
Recurrent Neural Networks (RNNs): To be used in sequence decision-making or for handling time-
series data.
Hybrid Models: For the combination of both CNNs and RNNs for those problems where there is
spatial as well as temporal awareness.
4. AI Algorithm Design and Implementation
Neural Network Design
Algorithms for deep learning are at the core of AI systems. Neural networks are programmed to:
Simulate the functioning of a human brain with various layers in interconnection.
Deal with immense, unorganized datasets such as images and video streams.
Hyperparameter Tuning
Key hyperparameters are:
Learning rate: Regulates how quickly the learning is done.
Batch size: Impacts model efficiency and execution time.
Number of layers and neurons: Regulates model complexity.
Training and Validations
Model training involves:
Sectored division of data into training set and validation set.
Monitor model performance for loss functions as well as accuracy.
Iteration of model improvements using backpropagation as well as the gradient descent algorithm.
5. Multi-Modal Sensor Integration
Importance of Sensor Fusion
Autonomous systems use many sensors to perceive their surroundings. Sensor fusion combines data
from a number of diverse and many sources to create an entirely unified view:
LiDAR along with cameras: LiDAR gives exact distance readings, as well as cameras give clear
picture data.
Ultrasonic Sensors can be helpful for spotting objects nearby. This includes in self-driving cars or for
unmanned aircraft.
When calibrated, many sensors work well together. For example:
Corresponding the fields of view of LiDAR and cameras enables accurate object recognition.
Matching data in time using GPSs and IMUs.
6. Simulation and Real-World Prototyping
Virtual Testing Environments
Simulations replicate real-world conditions, also providing a safe testing ground along with cost-
effectiveness. Tools include:
Gazebo: Accurately simulates most robotic movements within fairly detailed 3D environments.
ROS: Truly aids in fully testing robot algorithms and software.
After a suitable mock exercise, models are put through tests in observed, real-world settings:
Self-driving cars went through testing on some private courses.
Robots intended for healthcare get used within simulated hospital areas.
7. Safety and Risk Management Protocols
Mitigating Safety Risks
Safety protocols include:
Having redundant systems: Adding backup systems in for functions that are critical.
Careful Checks: Confirming that the system acts reliably in tough conditions, such as sudden sensor
failures.
Thorough hazard evaluations completely find possible dangers and mindfully create plans to lessen
them for guaranteeing AI systems are deployed safely.
8. Societal and Ethical Concerns
Transparency and Accountability
Decisions made by AI must be clear and explainable. Users should be informed about the reasons for
autonomous action.
Social Impact
The use of AI in autonomous systems has fears of losing jobs and unequal social impact. Developers
and policymakers must work together to address these issues.
9. Performance Measures and Benchmarking
Key Performance Indicators
Accuracy: The accuracy with which the system performs tasks assigned.
Latency: Time taken by the system to respond to inputs.
Robustness: Ability to handle changes of input data.
Benchmark Datasets
Use of standard datasets gives the same measurement for different AI models.
10. Scalability and Future Adaptability Framework
Modular Architecture
Modular architectures simplify it to:
Upgrading and scaling:
Adding new functionalities without reengineering the entire system.
Reconfiguring AI models for other applications, such as drones or underwater robots.
Real-Time Adaptation
Future systems will be required to have real-time learning, which will enable them to adapt
dynamically to changing conditions
CONCLUSION
AI has changed several sectors such as transport, healthcare, agriculture, and even space exploration
due to considerable reforms in autonomous systems and robotics. Autonomous robots can see, reason,
decide, and actuate, which changes how machines interact with particular environments, dealing with
complexity, with unprecedented precision and efficiency. The whole range of innovations led by core
technologies like machine learning, deep learning, and computer vision laid the ground for the
independent operations of autonomous systems in the real-world dynamic and unpredictable
situations, including processing sensory inputs and executing tasks. From cars that drive themselves,
drones, and robots to space rovers, the intelligent systems have massively revolutionized and further
raised limits for innovation across industries. Nonetheless, even with the upward trends, a lot must
still be achieved with regard to the assurance of system reliability and safety and addressing the
ethical concerns. The integration within society of autonomous systems does, however, raise issues
that ought to be addressed with care to promote responsible deployment, posing challenges related to
accountability, transparency, and the potential for social impact. This research demonstrates the
transformative potential of AI in autonomous systems and robotics; while simultaneously arguing for
continuous work required to achieve integration complexities. This field carries the hope for
balancing tomorrow's innovations with responsibility- a world where intelligent systems will
favourably compete with human beings to usher into reality the dream of a tech-empowered world of
progress toward an excellent quality of life.
