When It Comes to Robotics, Why Is Artificial Intelligence so Important?

Robotics is a multidisciplinary field which incorporates aspects of computer science, engineering, mechanical design, electrical design, and programmable electronic components. Despite the common misconception that robotics is a subset of Artificial Intelligence (AI), the two possess distinct focuses and applications. AI has been instrumental in the quick, precise, and effective growth of robotics technology. To understand the relevance of AI in robotics, expertise in both robotics and AI is necessary. This article will explore the relationship between AI and robots.

The Distinction between Robotics and Artificial Intelligence

Artificial Intelligence (AI) is a subfield of computer science and engineering focused on the development of programmatic devices that can emulate human intelligence. AI can enhance a device’s ability to perceive, comprehend, control, and remember information. There are four main categories of AI: reactive machines, limited memory, theory of mind, and self-awareness. Each of these will be discussed in detail in the following chapters.

Robotics, a subfield of engineering, focuses on manufacturing tools with predetermined functions. As these activities are monotonous and do not require any cognitive capabilities, the use of Artificial Intelligence (AI) is not necessary. However, when robots have to perform complex tasks, AI is employed. In actuality, AI is now widely used in robotics.

What roles do AI-powered robots play in the field of robotics technology?

Upon completion of a training or experimental phase, a robot that has been equipped with artificial intelligence algorithms may be able to act independently and make decisions without human intervention. By utilising the capabilities of machine learning, the robot can learn new skills, develop its ability to handle unfamiliar circumstances, gain a more profound comprehension of concepts, be able to reason and act in response to visuals.

In spite of the current lack of sophistication in the majority of robots, there is an increasing demand for intelligent automation in the business sector. Manufacturing and production are transitioning towards more mobile and self-governing robots that are able to analyse, assess, and handle data in order to make the best possible decisions. The capacity to simply lift weights is no longer the sole requirement for a robot’s efficiency.

The power of robotics is evidenced by their capacity to reason independently; this is one area of overlap between artificial intelligence and robotics. As more and more businesses seek to maximise efficiency, they are increasingly relying on robots to handle complex and sophisticated tasks that extend beyond automation.

Robots may find a plethora of advantages through the implementation of Artificial Intelligence (AI), including improved navigational capabilities, the capacity to accurately recognise objects, and even providing assistance to humans when performing tasks such as constructing drywalls, laying bricks, and undertaking robotic surgery.

What kind of relationship do robots and AI have with one another?

Since its inception in 1956 by John McCarthy, the term “artificial intelligence” has been the source of much debate and controversy. Skeptics of artificial intelligence cite a number of concerns, such as potential for prejudice and invasion of privacy, among other issues. Despite these apprehensions, we cannot deny the profound impact artificial intelligence has had on modern society.

The incorporation of computer vision in Artificial Intelligence (AI) enabled robots to benefit from navigation, assessment, and decision-making capabilities. Machine Learning (ML), a subset of both computer science and AI, is a powerful tool that enables robots to learn from and mimic human work habits and techniques. With the help of ML, robots can become more efficient, productive, and better at completing tasks.

Distinctions amongst AI Varieties

Depending on the desired outcome and the tasks that need to be completed, a wide range of Artificial Intelligence (AI) systems may be employed. Examples of these types of systems include those with a limited or reactive cognition or even those with a self-awareness. Currently, only the first two are in actual use, while the latter two remain purely theoretical. However, with the rapid advancement of AI, it appears that these possibilities may not be far off in the future.

Reactive

Robots which are able to respond to input from their environment are referred to as reactive robots. These robots lack the capacity for learning, remembering, or using prior experiences in order to address current needs. Despite their simplicity, these types of robots are widely employed in various applications. A prime example of this is the utilisation of spam philtres.

Recall isn’t very good

Machines with limited memory capacity can still carry out reactive functions, such as those performed by machines with more memory, but they can also benefit from past experiences. This type of Artificial Intelligence (AI) is employed in numerous applications used in everyday life, including autonomous cars and humanoid robots that can sense and react to their environment.

Mind-body theory

Despite significant advances in artificial intelligence, we have yet to attain the level of sophistication necessary for a theory of mind. To put it plainly, this refers to robots having the capacity to make decisions that are on par with those made by humans. Through this, robots would be able to understand people and their emotions.

Self-awareness

We have achieved remarkable advancements in artificial intelligence, enabling machines to reach a level of intelligence comparable to that of humans. These machines possess the capacity for self-reflection and abstract thought, making it difficult to distinguish them from us due to the similarity of their cognitive functions.

The role of artificial intelligence in robotics

The following instances show how AI is implemented in current generation robots.

Vision systems hardware and software

Previously, we discussed the use of computer vision technology, a subdivision within the realm of artificial intelligence, in order to enable robots to interpret visual stimuli. This technology allows robots to be trained to identify and analyse images and videos by recognising patterns and reacting to the data received. Computer vision has a wide range of applications in various industries, including but not limited to medicine and the entertainment industry.

Computer vision tasks

The list below includes some of the most important functions performed by computer vision systems.

  • OCR: An optical character reader (OCR) is a piece of software that can take a scanned document and turn it into something that can be edited.
  • Recognising faces: It’s useful for locating and recognising people in digital photos.
  • Reconciling Known objects: As a sub-field of computer vision, artificial intelligence (AI), and machine learning (ML), this research seeks to leverage AI models to identify and accurately name the most prominent elements present in a given digital image or video. By exploiting the power of AI and ML, this research aims to provide an automated system for object recognition and identification.
  • Estimating poses: This may be used to forecast where a person or thing will be and then follow its progress.

Language comprehension technology (NLP)

Natural language processing (NLP) is a subfield of artificial intelligence (AI) which enables robots to interact with humans. This technique allows robots to interpret and imitate human speech. NLP can be used so that humans can give voice commands to AI robots, which the robots then execute correctly. Famous examples of NLP technology are the voice assistants Siri and Alexa. These two assistants are well-known examples of natural language processing.

Computing in the Periphery

Due to the significant data and computing requirements of Artificial Intelligence (AI) applications, Edge Computing is an ideal solution to satisfy these needs. By addressing the technical obstacles faced by AI-based applications, Edge Computing has established itself as the premier way to tackle the following issues:

  1. One, less information is sent to the main cloud.

    The use of machine learning algorithms requires a significant amount of data in order to accurately identify patterns and provide meaningful advice. To reduce the cost of backhauling, more processing can be done on the edge, rather than in the cloud, which is especially important for applications that require heavy-duty video analysis, as these usually require an extensive amount of bandwidth to be sent to the cloud.
  2. In-the-moment deliberation

    When machine learning is utilised to initiate immediate actions, it is imperative that this happens with minimal delay. Rather than sending all the raw data to a distant cloud for processing, edge computing may allow judgments to be made at the point of origin of the data and the subsequent actions to be immediately activated on the edge.
  3. Storage and processing of data on a local level

    In comparison to cloud computing, edge computing provides an opportunity to store sensitive information, such as a customer’s location, locally. By running machine learning and artificial intelligence locally, only the most relevant data and insights need to be transmitted to the cloud, while the rest can remain stored locally.

Instances when AI-powered robots may be put to use

These days, robots can be found in almost every industry due to their accuracy and efficiency.

Medical robotics

The healthcare industry is undergoing a drastic transformation due to the introduction of robotic technology. Robots are capable of a wide range of medical and therapeutic applications, such as providing assistance to surgeons during delicate procedures, acting as prosthetic limbs, and aiding in rehabilitation. The possibilities are virtually limitless. An example of this technology in action is the da Vinci surgical system, which translates the surgeon’s hand motions at the control panel into precise movements that require a minimal amount of incision. This system has been used in a variety of surgical procedures, including those on the heart, brain, and other parts of the body.

Farming with robots

Agricultural tasks such as planting, weeding, and harvesting can be labour-intensive and time-consuming processes. Fortunately, new technological advancements are making it possible for such tasks to be handled more efficiently with robotics. For instance, the ecoRobotix is a robotic weed-whacker that can be powered by solar energy and is outfitted with a camera system to accurately identify and spray only the target weeds. Additionally, robots can be used in other areas of agricultural operations such as harvesting crops, applying pesticides, and assessing plant health. Ultimately, the application of artificial intelligence robots on the farm is helping to streamline and improve the agricultural process.

Using robots to streamline the automobile manufacturing process

For more than half a century, robots have been incorporated into the assembly process in the automotive industry. Currently, the industry is investing significant resources into exploring the potential of robots in areas such as assembly, welding, removal, transportation, and machine tending.

The use of robots in the storage facility

Warehouses have seen an increased need for manual labour in order to manage the massive amount of inventory necessary to supply the needs of e-commerce enterprises. While this has traditionally been a labour-intensive process, the introduction of robots has greatly reduced the amount of time and effort needed for performing tasks such as order selection, restocking, and cancelling stock. By training these robots to accurately maintain inventories and transfer items securely from one place to another, the need for human labour in such routine tasks is drastically reduced.

Armed forces’ use of robots

Robots are increasingly being utilised in the military for a variety of purposes such as surveillance and weaponized systems. Two prominent examples of such robots are the Modular Advanced Armed Robotic System (MAARS) and the Tactical Combat Robot (DOGO). MAARS has the appearance of a tank and is equipped with a range of features such as tear gas, laser dazzlers, aural deterrents, motion detectors and more. On the other hand, DOGO is outfitted with a 9 mm Glock, eight video cameras, and a remotely operated pepper spray module.

This article has highlighted the significant role that Artificial Intelligence (AI) plays in robotics. From healthcare to the military, exploration, entertainment, business, and customer service, robots are increasingly being used to provide high-tech solutions to a wide range of industries. As robots continue to evolve and develop, they offer an exciting prospect for the future of these sectors, and could be used to produce even more efficient and effective outcomes.

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