Robotics E-Learning Supported by Collaborative and Distributed Intelligent Environments

Robotics E-Learning Supported by Collaborative and Distributed Intelligent Environments

Maki K. Habib (The American University in Cairo, Egypt)
Copyright: © 2020 |Pages: 17
DOI: 10.4018/978-1-5225-7793-5.ch005
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Robotics e-learning environment that facilitates tailored learning for individual students studying robotics is developed. The developed collaborative and distributed intelligent environment (CoDIE) enables multi-users to access simultaneously remote and integrated mixed reality facilities through the web. The developed system constitutes a robotic center to help in transferring theoretical knowledge enhanced by simulation and practical experience. It enables realistic interaction by immersing users in a shared 3D CoDIE. The system enables users to do programming, simulations, experiments, manipulating data and objects, diagnostics and analyses, control and monitor actions. Also, users can receive feedback from the system or instructors. The developed system has been implemented and tested using two real manipulators and virtual robots supporting real-time tracking and simulation. Three modes of operations have been implemented, individual robot training mode through virtual robot models, multi-user mode working together, and individual or group-based training by instructor.
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There is no agreed universal definition of artificial intelligence (AI). However, in a general sense, it is the synergy of multiple technologies that together can create systems that can perform tasks (by applying algorithms and models to find patterns in large amounts of data) usually require human expertize. With the convergence of disruptive technologies (Robotics, nanotechnologies, genetics, 3D printing, etc.), the emergence of every new technologies are accelerating the growth and the capabilities of AI to address new opportunities and challenges (D2L 2018; Luckin et al, 2017). However, the new technologies disrupting the job market will always demand new skills. Hence, new evolution is taking place impacted by AI to lead the development transforming education, teaching and learning into a new era with aim to meet the need by enriching education techniques and methods. The use of AI in education and learning provides classrooms, teachers, schools and universities with innovative ways to understand how to support students’ learning process and assess their learning and skills as well as allowing better ways to enhance course content, individualized tutoring and facilitate tailored and adaptive learning experience. The principles of AI in higher education underlie a range of innovative systems that may include for example: analytics, robots, virtual experiences, advising, grading and intelligent tutoring (Wolf, 2009). AI will significantly influence what we teach and learn, as well as how better it can be done. Furthermore, the usage of machine learning, deep learning, and neural networks in AI systems beside the increased computational power represents effective AI-driven tools that facilitate and expand the opportunities to support and improve the education and learning process. New generation of students are living the new ear of education as they are growing up with the latest and smart technologies integrated through Internet and other networks.

Since the beginning of E-learning in 1997 it was associated with interactive learner-centered distance learning with aim to enhance knowledge and performance and it is expanded with the evolution of the information and communication technologies that support the growth of the Internet based technologies which evolved to support wide range of web-based applications using resources of various digital technologies (Mori, 1997; Rosenberg, 2001; Rossett, 2001; Cross, 2004; Khan, 2005). Robotics integrated with AI supporting automation along with service robots are influencing current and future industry, economies and society (Wolf, 2009).

This chapter aims to establish effective synergy between robotics, education with AI techniques by synergizing it with the integration of information and communication technologies with the necessary expertize beyond space and time constrains. Information, equipment, devices and expertize along with other resources are scattered across our planet and these resources may not be available where they are needed at the necessary time. The quality of communication technology and infrastructures of networking are forcing for definite move towards a rethinking of the physical structures and spaces needed for living, learning and working. As physical presence is no longer a prerequisite for physical influence there is a need to look for alternative forms of physical movement of people, knowledge, equipment, records and information, etc. This approach is evolving to address the issues of universal sense of connectivity and global access to information; easy control and use of globally linked physical devices, databases and processes, facilitating real time interaction and awareness along with supporting mobility and portability of people and enhance access to experts and other dispersed and valuable resources (Habib, 2008). Efforts to develop frameworks aiming to understand and design intelligent tutoring that facilitate robots tutoring with AI techniques. This is facilitated by the use the perception-planning-action to establish effective teaching-learning relationship between the education-learning domain and the robotics content domain (Truong, 2016).

Key Terms in this Chapter

Artificial Intelligence: Artificial intelligence (AI) is intelligence demonstrated by machines by adopting self-learning algorithms.

Augmented Reality: Is an interactive experience of a real-world environment where the objects that reside in the real-world are enhanced by computer-generated perceptual information.

Data Mining: Is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Industry 4.0: Is a name given to the current trend of automation and data exchange in manufacturing technologies.

E-Learning: It is interactive learner-centered interactive distance learning with aim to enhance knowledge and performance and it is expanded with the evolution of the information and communication technologies.

Artificial Neural Network (ANN): ANNs are computational networks composed of multiple nodes named neurons interacting with each other. The nodes can take input data and perform simple operations on the data.

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