Specialism Research Report: Robotics and AI

Introduction

Artificial Intelligence (AI) and robotics are some of the current advancements of the technology and the Internet within the fourth industrial revolution, and are set to revolutionize the human being’s way of life. Walch (2020) asserts that the incorporation of artificial intelligence into robots and robotics has allowed organizations and companies to move past activity automation and indulge in much more complex tasks such as design, trouble shooting and problem solving that can have new and far ranging impacts. Artificial intelligence and machine learning include software that enables previously mechanical pieces of hardware’s in robots to think on their own and therefore be able to aid in the completion of different tasks in an automation process (Walch, 2020). Such automated robots will in the future be able to provide adequate and complex services in almost all human disciplines and areas of survival including business, manufacturing, education, construction and others.

Open research Question

The open question in the specialized area of AI and Robotics is: Can the internet of Things (IoT) data through AI algorithms be developed into neural structures for service robots? The major collaboration front between AI and Robotics is the potential for AI algorithms and machine learning to enable robotics to become intelligent entities with the ability to think and undertake autonomous tasks. However this comes with a potential danger of singularity which has been a major subject of controversy since the development of AI and ML concepts. According to Harris (2019) all researchers are in agreement that robots can work in warehouses and assembly lines; however there is no consensus on whether robots can be made intelligent due to the potential consequences of singularity. Through adopting the data, knowledge and systems used within the IoT devices into robots and robotics, organizations can be able to develop intelligent robots that are task oriented and have a minimal capability of learning and reacting and as such bypassing the potential for singularity while achieving the same purpose. According to Harkiran (20190, IoT includes a network of multiple devices meant for different tasks and activities and that are connected over the internet to enable them collect and exchange data with each other while carrying out distinct tasks . All the already available and connected IoT devices generate a lot of data which can then be collected and mined for actionable results. This study sets out to find out whether Artificial Intelligence can be used to develop algorithms capable of converting the data from IoT devices to actionable results through a robots neural structure so as to enable it perform or implement a specific task

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Literature Review

Artificial Intelligence and Robotics as well as the Internet of things are intertwining technological concepts that have been a growing interest in multiple industries and field in the current business environment. According to Klein et al (2021) transport, agriculture, sports and film making are some of the industries that have hugely benefited with the advent of autonomous vehicles operating in different environments and able to help with multiple tasks. While self driven vehicles are yet to be adopted in the real world Autonomous surface vehicles (ASV) as well as Autonomous underwater vehicles (AUV) are currently being used for multiple applications including scientific research and ocean survey. Similarly unmanned aerial vehicles (UAV) are used in filming, sports broadcasting, emergency response, and product deliveries among other versatile applications. However these applications and devices still lack the autonomous aspect that would entirely eliminate the need for human intervention as seen in IoT. Applying IoT in developing robots would as such enable researchers to go beyond the threshold into new possibilities.

Mathews (2019) highlights that a strong value can be achieved by combining Robotics and Internet of Things (IoT) to create an Internet of Robotic Things. This is because the two fields are already driven by highly related objectives in variable perspectives and angles. While robotics designers are focused on supply chain actions and automation of behavior, the IoT is more focused on supporting services for pervasive sensing, tracking and monitoring. Combining the two can contribute to the development of an intelligent robot without the potential for an occurrence of singularity. Vermesan et al. (2020) advances that the IoRT will have three intelligence components including the ability to sense its possession of embedded monitoring capabilities with a wide database of sensor data from other sources for analysis.

The robot will also have the ability to instantly analyze data from the occurrences that it monitors through AI analysis algorithms. This instant analysis also means that the IoRT will have its data processed locally instead of remotely from a cloud server thereby eliminating a need for data transmission and making the reaction to different instances autonomous and almost instant (Mathews, 2019). The robot as a result of the first two components will also be able to determine the course and line of action enabling it to control and manipulate physical objects and even move them across the physical world. Some of the related problems after the implementation of the IoRT would be how achieve machine to machine and machine to man collaborations as well as how to achieve predictive repairs and maintenance and engage in entirely new services that the robot may not have been initially designed for.

Research Approach

The main research method that was used and can be used to investigate the question of whether IoT can be adopted through AI algorithms to develop a robots neural network is the secondary qualitative research method. This includes an extensive content analysis of the knowledge and information available within the three fields of IoT, Robotics and Artificial Intelligence. The information along with their possible connection and integration mechanisms were studied leading to the development of a possible technique of how to combine them to develop intelligent service robots. The research strategy is quite effective considering the multiple benefits that it offers especially for theoretical studies such as the one in hand.

According to Vaughan (2021) Qualitative secondary studies are significantly flexible given that they are not bound by the limitations of the quantitative research technique. If useful insights are not being adequately captures researchers can quickly adapt their questions and alter the parameters and any other variables to enhance the outcome. Further based on Rahman (2017) responses that don’t fit the studies expectations offer significant insight into the data’s context and help explain the intrinsic connection between such data that numbers are unable to reveal. Secondary qualitative studies are further extensively reliant on already existing knowledge and data derived from previous studies and capitalize in showing the relationship between such data. It is therefore a much faster and less costly research technique. Provoke insights (2020) further highlights that with the availability of the internet and multiple information sources, secondary qualitative research can help identify and gain information and data on almost any topic.

Some disadvantages of the research process however is that researchers may be unable to obtain information and data that is specific to what they are looking for. In addition, the existing research and information available may not have the currency necessary to be useful (Provoke Insights, 2020). This is however a disadvantage that is adequately countered within the study given that the topic pursued is a current and relevant topic with multiple information sources that enhance ultimate efficiency of the study process. To build on this research and answer related open questions I would indulge in an experimental research design involving experts to help create the actual robot with IoT and AI technology.

Personal Investment

I am particularly interested in this research topic as a result of its currency and potential to have a significant impact in multiple aspects of the current human beings life. Automated Robotics is the future of technology and development and one that will be adopted to ensure adequate automation of business, learning, agricultural, construction and manufacturing processes among others would have significant impacts. I also have a distinct interest in AI and how it can be safely and effectively used to solve some of the problems experienced by humanity such as climate change and global warming. It can aid in the brainstorming of developing efficient energy resources or contribute towards the efforts of cleaning up toxic substances across the environment. My knowledge and significant understanding of all the three involved concepts in IoT, AI and Robotics makes me adequately qualified to carry out this research study.

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References

Harkiran, 2019. 8 Best Topics for Research and Thesis in Artificial Intelligence - GeeksforGeeks. [online] GeeksforGeeks. Available at: [Accessed 11 November 2021].

Harris, T., 2019. How Robots Work. [online] HowStuffWorks. Available at: [Accessed 11 November 2021].

Klein, I., Faigl, J., Fieldman, D. and Salzman, O., 2021. Autonomous Vehicle Navigation and Sensor Fusion. [online] Frontiers. Available at: [Accessed 11 November 2021].

Matthews, K., 2021. The Internet of Robotic Things: How IoT and Robotics Tech Are Evolving Together. [online] IoT Times. Available at: [Accessed 11 November 2021].

Provoke Insights, 2020. The Pros and Cons of Secondary Research. [online] Provoke Insights. Available at: [Accessed 11 November 2021].

Rahman S. (2016). The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language “Testing and Assessment” Research: A Literature Review. Journal of Education and Learning. 6(1) pp 102-112.

Vaughan, T., 2021. 10 Advantages and Disadvantages of Qualitative Research | Poppulo. [online] Poppulo.com. Available at: [Accessed 11 November 2021].

Vermesan, O., Bahr, R., Ottella, M., Serrano, M., Karlsen, T., Wahlstrøm, T., Sand, H., Ashwathnarayan, M. and Gamba, M., 2020. Internet of Robotic Things Intelligent Connectivity and Platforms. Frontiers in Robotics and AI, 7.

Walch, K., 2020. Application of AI in robotics boosts enterprise potential. [online] SearchEnterpriseAI. Available at: [Accessed 11 November 2021].


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