AI-Enabled Robotics

Introduction

Artificial intelligence (AI) stands at the forefront of technological evolution, encompassing a broad spectrum of disciplines within computer science. At its core, AI seeks to emulate human intelligence in machines, enabling them to perform tasks that traditionally required human cognitive abilities. The field is characterized by various sub-disciplines, with machine learning, natural language processing, and computer vision being integral components. Machine learning involves the development of algorithms that enable machines to learn from data, adapt, and improve performance over time. Natural language processing focuses on the interaction between computers and human language, allowing machines to understand, interpret, and generate human-like text. Meanwhile, computer vision empowers machines to interpret and make decisions based on visual data, expanding their capabilities to recognize patterns and objects (J. Howard, 2019).

The practical applications of AI are vast and ever-expanding. Speech recognition systems, driven by AI algorithms, have become integral components of virtual assistants like Siri and Alexa. Image and pattern recognition, another facet of AI, is evident in technologies such as facial recognition and object identification in images (Arinez et.al, 2020). AI is a driving force in the development of autonomous vehicles, contributing to navigation, obstacle detection, and decision-making processes. Healthcare benefits from AI through medical diagnosis, drug discovery, and personalized medicine, where machine learning algorithms analyze vast datasets to identify patterns and provide insights(Andras et.al, 2020).

However, the proliferation of AI is not without its challenges. Ethical concerns have risen to the forefront, encompassing issues such as privacy, bias in algorithms, and the potential for job displacement. The explainability of AI systems, particularly in deep learning, poses a significant challenge, as these algorithms are often perceived as “black boxes” due to their complex decision-making processes. Ensuring the safety and security of AI systems, especially in critical applications like healthcare and autonomous vehicles, is a paramount consideration (Q. Bai et.al, 2020).

On the other hand, Robotics, as a distinct discipline, focuses on the design, construction, and operation of robots. A robot is a programmable or virtual agent equipped with sensors for perception and actuators for interaction with the environment. The field encompasses diverse applications, ranging from industrial automation and healthcare to space exploration and agriculture. Industrial robots, for example, have revolutionized manufacturing processes by performing tasks such as assembly, welding, and material handling with precision and efficiency. In healthcare, robots are employed in surgical procedures, rehabilitation, and assistance for individuals with disabilities. The use of robotic systems in space exploration, including rovers and unmanned spacecraft, allows for the exploration and study of distant planets and celestial bodies (Lee et.al, 2019).

Despite the progress, challenges persist in the field of robotics. The complexity of tasks, especially those requiring manipulation of diverse objects or navigation in unpredictable environments, remains a significant hurdle. Ensuring interoperability between different robotic systems is crucial, particularly in scenarios where diverse robots need to collaborate seamlessly. Cost considerations also pose challenges, limiting the widespread adoption of robotic systems, particularly for small and medium-sized enterprises (Jiang, 2020).

Figure 1.

Architecture of AI and robotics communications

The merging of AI and robotics represents a paradigm shift in technological capabilities. The integration of AI into robotics enhances the intelligence and adaptability of robotic systems. Machine learning algorithms play a vital role in enabling robots to learn from data and improve their performance, making them more adaptable to unpredictable tasks and environments. Computer vision allows robots to perceive and understand their surroundings, enabling tasks such as object recognition and navigation (Panesar et.al, 2019).