Avances en robótica y automatización

Auto planning is a branch examination of planning which shows manufacturing, arranging, mechanical instruments similarly as exercises of cars. It is a preface to vehicle planning which oversees cruisers, vehicles, transports, trucks, etc. It fuses branch examination of mechanical, electronic, programming and prosperity parts. A piece of the planning credits and instructs that are of importance to the auto fashioner and huge quantities of various perspectives are associated with it.

Mobile robots are becoming more and more part of our industrial world. With the development of robotics, the design of the mobile robot
has become conditioned to the function it has to fulfill. It is important to make a study and understand the difference concepts found in robotics
in order to design the most suitable mobile robot for a certain industry. Holonomy is one of these concepts discussed in this paper.
Holonomic and non-holonomic mobile robots use different wheels where the first is omnidirectional and the latter is limited with its
movement. And thus, which robot to use remains a question in the minds of those running warehouses. In addition to that, it is worth to
explore the different sensors used on vehicles in order to covert them to automated guided vehicles. Understanding the concept of
holonomy, knowing the difference in the performance of holonomic and non- holonomic robots , as well as AGVs systems help in making the
decision suitable for the warehouses.

As AI continues to progress, one of the challenges we face is to involve robotics to be automated the tasks that are repetitive, dangerous,
or vulnerable to human error. However, automation without intelligence creates a system that cannot respond to variables, new
environments, or dynamic requirements. So AI provides a perfect platform to develop intelligent bots. By adding cognitive services to the bot,
we can make our bot smart with capabilities like language understanding, image recognition, text recognition, translation, and more.

In many develop nation share of older people is increasing by 0.2
percent every year and 2 percent over a period of decade. Also, there
has been a decline in life expectancy in member states over the time.
With the increase in disordered lifestyle, psychological
issues, genetic problems, infectious diseases, climate change and
regular pandemic/ epidemic the quality of life is deteriorating. Just
to add, recent pandemic is biggest example to make world realize
that we right now in our biggest time to rethink about a robust,
resilient, advanced and intelligent healthcare ecosystem. To cater the
need the issues for healthcare ecosystem is currently dealing
with data fragmentation, data accessibility issues, fraud issues,
manual entry processes, unsynchronized approach, highly
complex compliance frameworks, hideous information, inefficient
processes creating delays in message passing, data security,
breaches and importantly lots of administrative work done by the
medical professionals which limits research.

There is a constant change in the techniques of teaching and learning as a result of artificial Intelligence (AI). In the past, the objective
was to provide an intelligent tutoring system without intervention from a human teacher to enhance skills, control, knowledge
construction, with emotional and intellectual engagement. The present study focuses on enhancing the learning capabilities of
humanoid robot Nao and increasing his intelligence by activating multi-sensory perceptions among 3 to 7 years old children. Here, the multisensory
perceptions include; visual and auditory stimuli modules, speech-related, and body movements. The design and testing processes
were conducted by implementing an AI principle design, namely the three-constituent principle. The study has developed a toolkit that uses a
mixed reality system architecture featuring different ways of interaction between a child and a robot Nao agent. The toolkit enables Arabic
speech recognition, and the Haar algorithm was implemented for robust image recognition to improve the capabilities of the Nao during
interactions with a child in a mixed reality system.

Particle Swarm Optimization (PSO) for Intelligent Control of agent autonomous rehabilitation robot is a very complex problem, especially for
stroke patients’ treatments and dealing with real-time EMG sensors readings of muscles activity states and transfer between real-time Human
motions to interface with rehabilitation robot agent or assisted-device. The field of Artificial Intelligence and neural networks plays a
critical role in modern intelligent control interfaces for robot devices. This paper presents a novel hybrid intelligent robot control that acts as
human-robot interaction, where it depends on real-time EMG sensor patients data and extracted features along with estimated knee joint angles
from Extended Kalman Filter method are used for training the intelligent controller using support vector machines trained with Adatron
Learning algorithm for handling huge data values of sensors readings. Moreover, the proposed platform for rehabilitation robot agent is tested
in the framework of the NAO Humanoid Robot agent along with Neurosolutions Toolkit and matlab code. The average overall accuracy of the
proposed intelligent motion SVM-EKF controller shows average high performance that approaches average 96% of knee motions
classifications and also good performance for comparing Extended Kalman filter knee joint angles estimations and real EMG human knee joint
angles in the framework of Human Walk Gait cycle. Also, the basic enhancement of proposing PSO optimization technique for robot knee
motion is discussed for future improvements. The overall algorithm, methodology and experiments are presented in this paper along with
future work.