AI in Robotics: Use of Artificial Intelligence in Robotics VSINGHBISEN

Personal assistants, such as Siri and Alexa, help with searching for information, making orders and controlling devices in smart homes. The development of such assistants will obviously not stop, as consumer demand is growing. The first simple robots stepped onto the factory floor approximately half a century ago. Today, one can hardly imagine a manufacturing business without automatic lines, steel robotic arms and CNC industrial machines. Robots in the manufacturing industry have become the new normal, and they are making significant strides in improving various production processes. Scientists, industry experts and ordinary people all express various viewpoints about the potential outcomes of the active AI and robotics development.

They have mechanical structures, electronic systems, and programming languages as a backend. In addition, robotics is a synthesis of electrical engineering, mechanical engineering, and computer science and engineering. AI robots can also assist patients’ recovery by monitoring vitals, delivering medication, and tending to their needs. Futronics has developed an AI ecosystem for the healthcare industry that handles patient monitoring and care.

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Intelligent robots have been successfully constructed using machine learning and deep learning AI technology. Robotics performance is improving as higher quality, and more precise machine learning processes are used to train computer vision models to recognize different things and carry out operations correctly with the desired outcome. This book addresses many applications of artificial intelligence in robotics, namely AI using visual and motional input. Robotic technology has made significant contributions to daily living, industrial uses, and medicinal applications. Machine learning, in particular, is critical robots or unmanned/autonomous systems such as UAVs, UGVs, UUVs, cooperative robots, and so on.

Many organizations integrate AI robots into their routine procedures for increased productivity, efficiency, and better customer experience. Dominating both tech and non-tech industries, robots can be seen greeting customers at stores, waiting tables at restaurants, harvesting crops, or lifting heavy loads at manufacturing plants. In industrial settings, AI-enabled robots keep workers safe by operating in shared spaces. They also perform complex tasks such as cutting, grinding, welding, and inspection autonomously. A research study concluded that the introduction of 1.34 robots per 1000 workers  (one standard deviation) reduced workplace injuries by approximately 1.2 injuries per worker. The intersection of robotics and artificial intelligence (AI) is quickly becoming a driving force in the creation of new industries, cutting-edge technologies and increased productivity and efficiency in existing sectors.

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This allows robots to operate autonomously, without the need for constant human supervision or intervention. Various AI and robotic systems support rehabilitation tasks such as monitoring, risk prevention, or treatment (23). For example, fall detection systems (24) use smart sensors placed within an environment or in a wearable device, and automatically alert medical staff, emergency services, or family members if assistance is required. AI allows these systems to learn the normal behavioral patterns and characteristics of individuals over time. Moreover, systems can assess environmental risks, such as household lights that are off or proximity to fall hazards (e.g., stairwells). Physical systems can provide physical assistance (e.g., lifting items, opening doors), monitoring, and therapeutic social functions (25).

AI-based chatbots and consulting applications facilitate the branch of telemedicine. Other intelligent programs can perform precise diagnostics by analyzing patients’ medical records and other data, e.g. from medical wearables. The main goal of emerging technologies is not to replace humans in their jobs, but to make all processes safer and more efficient. It’s not a confrontation, but rather a beneficial collaboration between automated robotics and humans. Traditional programming of robotic systems can be time-consuming and require specialized knowledge. With AI, systems can be trained using a variety of methods, such as demonstration or through learning from simulation, reducing the time and expertise required for setup.

Learning From Demonstration

Precision medicine considers the individual patients, their genomic variations as well as contributing factors (age, gender, ethnicity, etc.), and tailors interventions accordingly (8). Digital health applications can also incorporate data such as emotional state, activity, food intake, etc. Given the amount and complexity of data this requires, AI can learn from comprehensive datasets to predict risks and identify the optimal treatment strategy (36). Clinical decision support systems (CDSS) that integrate AI can provide differential diagnoses, recognize early warning signs of patient morbidity or mortality, or identify abnormalities in radiological images or laboratory test results (37). They can increase patient safety, for example by reducing medication or prescription errors or adverse events and can increase care consistency and efficiency (38).

• This work has been demonstrated for many years, with impressive results, at the MIT Media Lab under Prof. Hugh Herr12 However, those applications have rarely left the lab environment due to the device cost.
• Equipped with an autonomous real-time navigation system, Restore-L has algorithms, sensors, and a processor helping it find its way, using its multifunction tools and dextrous arms to grab, refuel, service, and relocate.
• One example of new ways to prepare students for a digital future is IBM’s Teacher Advisor program, utilizing Watson’s free online tools to help teachers bring the latest knowledge into the classroom.

In this method, a user first outlines objects with virtual boxes using an AR pen (covered with markers) and a robot acquires different camera poses through scanning the environment. These images are used to augment bounding boxes on a GUI which enables the user to refine them. The used system incorporates a Learning and Support Module which learns from previous user-interactions and supports users through recommending new virtual borders. The borders will be augmented on the live stream and the user can directly select and integrate them to the Occupancy Grid Map (OGM).

Over the last decade, the use of AI in robotics applications in the entertainment industry has steadily increased. Artificial intelligence in robotics aids models in learning to perform specific tasks and makes robots more intelligent in a variety of environments. Artificial intelligence (AI) has been applied in the field of robotics to enable robots to perform tasks that require intelligence and adaptability. AI-powered robots can be used in a wide range of applications, including manufacturing, healthcare, agriculture, and defense.

Robotics, on the other hand, refers to the development of robots that can perform specific physical tasks. These robots can be programmed to carry out simple, repetitive actions, such as sorting items or assembling miniscule parts. While AI can be integrated into robotics to enhance the robot’s capabilities and improve decision-making, it’s not always necessary.

This programmed machine gets input automatically and applies the same according to the program. These machines operate on computer stimulation and their inputs are in the shape of symbols and rules. Specific software algorithms are used in machines which use artificial intelligence technology. AI in robotics has automated several industries using industrial-grade robots combined with artificial intelligence. AI has revolutionized these industries by shifting complex, resource-intensive, and laborious tasks to machines that can operate for long hours and with high precision.

That’s possible through computer vision’s object-detection ability, which could benefit almost any area of manufacturing by identifying product flaws or machinery that needs repairs. Computer vision’s object tracking—the ability to follow an object once detected—is essential for cobots (“collaborative robots” intended for direct human/robot interaction within a shared space), as well as autonomous vehicles and drones. Now that high-resolution cameras and high-powered GPUs are common and not prohibitively expensive, machine-learning computer-vision systems can often inspect products for defects better than people can.

It is estimated that there will be 3 million industrial robots in operation during 2020. Furthermore, Gartner projected that RPA software spending was over $1.3 billion in 2019. As such, the need and desire for bots of all sorts is seemingly only to going to increase. Physical robots have been around for almost a hundred years, but not without their limitations.

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