Introduction
The interrelationship between the artificial intelligence (AI)
and robotics is fundamental to the development of autonomous human-robot
systems. While AI provides the cognitive capabilities for perception,
decision-making, and learning, robotics offers the physical capabilities for
interaction with the real world. Cognitive technology is a field of robotics
concerned with endowing a robot with intelligent behavior by providing it with
a processing architecture to the chosen environment (Cangelosi & Asada,
2022). The cognitive systems allow the robots to learn and reason about how to
behave in response to complex goals in a complex world. However, the
technologies driving these systems have their strengths and weaknesses, and
addressing these challenges is crucial for advancing the field and ensuring the
safe and effective integration of robots into society (Krishna Pasupuleti,
2024). Thus, the paper will present a report that was prepared after designing design
and implementing a routine for a robot in Webots. The report will focus on discussing
how the task performed by the designed robot, its significance, challenges
faced, and potential real-world applications.
The project demonstrates an integration of robotics,
computer vision, and machine learning to achieve autonomous navigation and
object recognition. The robot was equipped with various sensors and a camera to
follow objects and predict numbers.it was designed to processes camera data to
predict a number using the logistic regression model. The robot starts and
initializes its sensors and wheels for movement which are used to rotate to
face north and follows any recognized object until it reaches the specified proximity.
Upon reaching the object, the robot captures an image, processes it, and
predicts the number using the logistic regression model. The robot then centers
itself and repeats the process for east, south, and west directions (Krishna
Pasupuleti, 2024). The process continues in a loop, allowing the robot to
navigate, follow objects, and predict numbers autonomously.as a result, the
developed routine aimed to create a highly capable and autonomous robot that
can navigate its environment, recognize and follow objects, and predict numbers
with high accuracy. The project integrates object recognition capabilities to
identify and follow objects with specific colors and predict numbers on them,
showcasing the application of cognitive systems in visual processing and
decision-making.
Literature Review
Many researchers and practical applications in cognitive systems
and robotics have demonstrated that the Artificial Intelligence (AI) and robotics
are intrinsically linked. AI provides the "brain" for robots, enabling
them to make decisions, learn from experiences, and adapt to new situations,
while robotics offers the physical "body" that executes tasks in the
real world (Soori et al., 2023). For instance, the AI techniques such as
computer vision and sensor fusion help robots perceive and understand their
environment. AI algorithms process data from cameras, radar, and other sensors
to identify objects, navigate spaces, and interact with humans and other
objects. Similarly, the AI-driven algorithms such as machine learning, deep
learning, and reinforcement learning, allow robots to make informed decisions
and plan actions. These technologies enable robots to optimize paths, avoid
obstacles, and perform complex tasks autonomously.
The research also shows that robotics relies on AI for
precise control of actuators and motors. AI models help in controlling
movements, ensuring smooth and accurate execution of tasks, and adjusting
actions based on real-time feedback. According to Tawiah, (2022), the cognitive
system used through AI enhances the interaction between humans and robots. For instance,
the natural language processing (NLP) and speech recognition enable robots to
understand and respond to human commands, while AI-driven emotion recognition
helps robots to interpret and react to human emotions. Additionally, the AI
allows robots to learn from their experiences and adapt to new environments and
tasks. Machine learning algorithms enable robots to improve their performance
over time without explicit programming for every new task.
Robots have been extensively used in manufacturing for tasks
such as assembly, welding, painting, and quality inspection. Cognitive robotics
is a form of embodied cognition which exploits the robot's physical morphology,
kinematics, and dynamics, as well as the environment in which it is operating,
to achieve its key characteristic of adaptive anticipatory interactions. Cognitive
robots are highly efficient, capable of performing complex tasks in less time,
often surpassing human capabilities (Tawiah, 2022). The efficiencies precision
and consistencies make cognitive robots cost-effective over time, even with
initial investment costs. According to Krishna Pasupuleti, (2024) the research,
Artificial Intelligence (AI) models and architectures add significant value to
interactive systems across various domains. However, the value of the robot can
be critically appraised by examining the enhancements in user experience,
efficiency, personalization, scalability, and decision-making capabilities.
Robots have been extensively used in manufacturing for tasks
such as assembly, welding, painting, and quality inspection. Collaborative
robots work alongside humans, enhancing productivity and safety while the human-robot
models and architectures have a broad and growing range of applications in
industrial and commercial environments. The effectiveness and benefits of
utilizing the robots are evident in increased efficiency, precision, and cost
savings across various sectors. However, limitations such as high initial
costs, integration complexities, and ethical considerations must be addressed. The
future of the applications lies in overcoming these challenges through
technological advancements, regulatory support, and societal acceptance, paving
the way for more widespread and effective deployment of human-robot systems.
Description of implementation
The project integrates object recognition capabilities to
identify and follow objects with specific colors and predict numbers on them,
showcasing the application of cognitive systems in visual processing and
decision-making. The robot was equipped with various sensors and a camera to
follow objects and predict numbers. The robot uses a logistic regression model
for number prediction, which is loaded at the beginning. The main functions
involve rotating the robot, following objects, processing images, and
predicting numbers. It was designed to utilize sensors and actuators such as
inertial unit, GPS, distance sensors, ground sensors, and the camera. The feature
made it possible to develop the robot’s ability to autonomously navigate its
environment using sensors and a pre-defined routine. The project also implemented
robust image processing techniques to convert camera data into a format
suitable for the machine learning model. The model combines data from various
sensors (inertial unit, GPS, ground sensors, and camera) to enhance the robot's
decision-making and navigation capabilities.
The project also implemented object recognition capabilities
using the robot's camera to identify and track objects with specific colors.it
was designed to ensure precise movement and positioning using yaw data from the
inertial unit and proximity information from distance sensors. The system was
also designed to initializes the motors for the robot's wheels to allow
movement. It also utilizes the utility functions such as “wait” to pause the
robot, ‘get_yaw’ to get the current orientation, and “rotate_to” to rotate the
robot to a specified direction. The project was also designed to utilize the
movement functions to turn the robot in specific directions. The functions
included the “followObjectUntilDestination” which allows the robot to follow an
object until it reaches a specified tolerance.
The project also integrated
a pre-trained logistic regression model to predict numbers on objects based on
images captured by the robot. The functionalities also included the “processObject”
which captures an image, saves it, and predicts the number using the logistic
regression model. The function “predict Number” was also implemented to processes
camera data to predict a number using the logistic regression model. Similarly,
the funtion “centerLocation” was implemented to reorients the robot based on
ground sensor readings. The main loop directs the robot to follow objects in
four directions (north, east, south, and west) and reorient itself after each
movement as shown by the image above. It processes objects and predicts numbers
at each destination
Advantages and disadvantages
Advantages
·
One of the advantages of using the robot include
the efficient object following which enable the robot to follow objects
smoothly and efficiently, adjusting its speed and direction based on real-time
sensor data.
·
Accurate
Number Prediction where the robot can achieve high accuracy in
predicting numbers on objects by processing images and extracting relevant
features for the logistic regression model.
·
Seamless
Integration and Execution which ensure seamless integration of
navigation, object recognition, and number prediction routines, allowing the
robot to perform tasks autonomously without human intervention.
·
Another advantage
include the adaptability of the robot to
different environments by designing the robot's routine to be adaptable
to different environments and object configurations which ensures reliable
performance in varied conditions.
·
The focus
was also placed on producing user-friendly and maintainable codes by developing
clean, well-documented, and maintainable code to facilitate future enhancements
and debugging. Efficiently planning paths in real-time to avoid delays and
optimize routes requires sophisticated algorithms and real-time processing
capabilities.
·
Enable real-time decision-making capabilities,
allowing the robot to react quickly to changes in its environment and object
positions. The robot can automate repetitive tasks such as sorting, picking,
and placing items, significantly increasing productivity and reducing human
error.
·
The project also focused on optimizing the
robot's movements and processing routines to conserve energy and extend
operational time.
Disadvantages
·
Integrating the robot with existing warehouse
management systems (WMS) and enterprise resource planning (ERP) systems can be
complex, requiring custom interfaces and protocols.
·
Ensuring that the system can scale with
increasing warehouse size and complexity is essential for long-term usability.
·
Efficient battery management is critical to
ensure the robot can operate for extended periods without frequent recharging. Regular
maintenance and updates are required to keep the robot functioning optimally,
which can be resource-intensive.
·
Varying lighting conditions can affect the robot's
camera performance, potentially leading to errors in object recognition.
·
Warehouses contain a wide variety of objects in
different shapes, sizes, and colors. The robot must be capable of accurately
identifying and handling diverse items.
Use of the robot in the real world
The project integrates object recognition capabilities to
identify and follow objects with specific colors and predict numbers on them,
showcasing the application of cognitive systems in visual processing and
decision-making. The robot uses data from multiple sensors (inertial unit, GPS,
distance sensors, and camera) to navigate autonomously, demonstrating the
importance of cognitive systems in enabling real-time decision-making and
movement. The inclusion of a logistic regression model for number prediction
exemplifies how cognitive systems can process and analyze visual data to make
accurate predictions, enhancing the robot's functionality.
The project showcases precise
movement and positioning through the use of yaw data and distance sensors,
highlighting the role of cognitive systems in ensuring accurate navigation and
task execution. The robot's ability to follow objects, predict numbers, and
navigate back to a home position seamlessly integrates multiple tasks,
illustrating the power of cognitive systems in managing complex workflows. Therefore,
the project's practical implementation demonstrates how cognitive systems can
be used in real-world applications, such as warehouse management, automated
inspection, and service robots, providing valuable insights for future developments.
The project underscores the critical role of cognitive systems in enhancing the
capabilities, efficiency, and autonomy of robotic systems, paving the way for
more advanced and intelligent robots in various domains.
The robot can be used to
identify and sort objects based on color and numbers, improving efficiency in
warehouse operations. With its ability to navigate autonomously and recognize
objects, the robot can help in real-time inventory tracking and management. The
robot can also be deployed in manufacturing plants to inspect products for
defects based on visual attributes, ensuring high quality and reducing waste.
It can also be used to inspect infrastructure such as pipelines, bridges, and
buildings for damage or wear, improving maintenance and safety.
In healthcare and assisted living facilities, the robot can
assist in delivering items to patients, tracking their locations, and ensuring
their safety. The robot can be used in retail environments to guide customers
to products, restock shelves, and provide inventory updates.
New Implications and Emerging Opportunities
The integration of more advanced AI algorithms can enable
the robot to learn from its environment continuously, improving its performance
over time. The robot can also be part of a fleet of collaborative robots that
work together to complete tasks more efficiently, leveraging swarm intelligence
principles. The research also shows that further advancements in computer
vision are necessary to improve the accuracy and speed of object recognition. Similarly,
it was also clear that the enhanced machine learning techniques can provide
better predictive models for the robot's tasks, improving its decision-making
capabilities.
Moreover, improvements
in sensor technology can enhance the robot's ability to navigate and interact
with its environment more precisely. Indebt Research into human-robot
interaction can help design robots that are more intuitive and user-friendly,
improving their acceptance and effectiveness.
Ethical implications
The deployment of such robots could lead to job displacement
in industries like manufacturing, warehousing, and retail, raising concerns
about the social impact of automation. Similarly, the robots equipped with
cameras and sensors could collect vast amounts of data, leading to potential
privacy concerns and the need for stringent data protection measures. As robots
become more autonomous, ensuring that their decision-making processes are
transparent, fair, and ethical is crucial. The ethical considerations include
avoiding biases in AI algorithms and making ethical choices in uncertain
situations. Ensuring the safety of robots in human environments is paramount.
Clear guidelines and regulations are needed to determine liability in case of
accidents or malfunctions. Besides, ensuring that the benefits of robotic
automation are accessible to a wide range of users, including those in
developing regions is important to prevent widening the technological gap.
Conclusion
Given the growing need for robots that can interact safely
with people in everyday situations, the project underscores the critical role
of cognitive systems in enhancing the capabilities, efficiency, and autonomy of
robotic systems, paving the way for more advanced and intelligent robots in
various domains. The robot developed in this project has significant potential
for real-world applications, from warehouse automation to assisted living. A
key feature of cognitive robotics includes its focus on predictive capabilities
to augment immediate sensory-motor experience. However, its deployment
raises various new implications and challenges that need to be addressed
through advances in supporting sciences such as computer vision, machine
learning, and human factors engineering. Ethical considerations around job
displacement, privacy, decision-making autonomy, safety, liability, and
equitable access must also be carefully managed to ensure the responsible
development and use of robotics technology.
The application of human-robot models and architectures in
industry and commercial environments has seen significant growth and
diversification. Evaluating the extent to which these models can be applied
involves assessing their effectiveness, benefits, limitations, and real-world
examples.
References
Cangelosi, A. and Asada, M.
(2022) ‘What is Cognitive Robotics?’, Cognitive Robotics, pp. 3–18.
doi:10.7551/mitpress/13780.003.0005.
Krishna Pasupuleti, M.
(2024) ‘Next-generation cognitive robotics: Advanced Technologies and
Applications’, Advanced Cognitive Robotics, pp. 12–21.
doi:10.62311/nesx/22645.
Soori, M., Arezoo, B. and
Dastres, R. (2023) ‘Artificial Intelligence, Machine Learning and deep learning
in advanced robotics, a review’, Cognitive Robotics, 3, pp. 54–70.
doi:10.1016/j.cogr.2023.04.001.
Tawiah, T. (2022) ‘Machine
Learning and Cognitive Robotics: Opportunities and challenges’, Cognitive
Robotics and Adaptive Behaviors [Preprint]. doi:10.5772/intechopen.107147.
