Robotics and AI

The robotic system NYSCF has developed enables us to generate enormous volumes of data from big patient groups and discover novel disease signatures as an altogether new basis for finding effective medications. This shows a perfect illustration of the power of artificial intelligence for illness research.

1) Medical Microrobots: Biodegradable microrobots that can dissolve into the body after carrying cells and drugs have been mass-produced by scientists. Building microrobots with the aim of minimally invasive targeted precision treatment can be done in a variety of ways. A technique that can make more than 100 microrobots per minute that can dissolve in the body must be developed. The most well-known of them is the two-photon polymerization method, which causes polymerization in synthetic resin by intersecting two lasers. It is an ultra-fine 3D printing technique. This method can produce structures with nanometer-level precision. The disadvantage is that producing a single microrobot takes a long time since voxels, the 3D-printed pixels, need to be successively cured.

2) Biorobots: The ability of muscle cells to flex in reaction to light has been genetically altered by researchers. This could one day lead to highly articulated, flexible robots. Endoscopy, a process where a camera is put through the body to illuminate tissue or organs, may be one potential robotic gadget. Many robotic designs draw inspiration from nature, such as geckos that adhere to walls, tuna that swim through water, and cheetahs that run across the ground quickly. Such designs use developed components and hardware to simulate the behaviour of animals while utilising natural qualities.

Shared-laughter AI system developed by scientists of Japan. Of course, conversation involves more than merely replying correctly. We therefore came to the conclusion that sharing laughing with consumers is one way a robot may relate to them, which cannot be done with a text-based chatbot.

Development of Soft Robotics: New Shape-Morphing, Self-Healing, Intelligent Material. A new class of stretchy materials is needed in order to advance the disciplines of soft robotics, wearable technology, and human/machine interactions. These materials must be able to alter shape adaptively while just requiring portable electronics for power. A material with this particular combination of outstanding electrical and thermal conductivity and unmatched actuation capability has been created by great researchers.

 To promote 3D printing for biomedical applications or biomedical injection materials, future research will try to improve this process of combining homocomposite materials.

After the concept was introduced, a number of sectors have examined the fourth industrial revolution and the effects of the technologies and drivers driving Industry 4.0. Through early stakeholder involvement, vertical and horizontal integration, and real-time asset and process monitoring, the objective is to enable autonomous decision-making processes, equally real-time connected value creation networks, and real-time asset and process monitoring. The technologies that enable Industry 4.0 use existing data as well as a variety of additional data sources, such as data from connected assets, to improve manufacturing processes, provide end-to-end information streams across the value chain, and introduce new services and business models.

1) AI and Robotics in upcoming Industry: Robotics for Space Mission are used and explored for its more advanced applications keeping in consideration of the type of terrain it will encounter in space. It has been used to collect data and to go into the region where human reach is not possible based on temperature and environment. Swarm of tiny swimming robots may search for extraterrestrial life on other or distant worlds.

2) AI and Robotics for renewable energy technologies: Combination of machine learning and robotics to study metal halide pervoskites, or thin, lightweight, flexible materials with outstanding properties for harnessing light which can be used to make sensors, solar cells, and energy-efficient lighting. It is to enhance material optimization and discovery to modify materials for increased efficiency.

The significance of neural networks is discussed at the Artificial Intelligence Conference. They are encephalon-inspired systems designed to mimic the way people learn, as the "neural" portion of their name denotes. In addition to input and output layers, neural networks also have an optimised layer made up of components that convert the input into something the output layer can use. These are the systems that were created using the encephalon's neuronal operation as inspiration, and they will imitate how people learn. Input & output layers are both present in neural networks, along with obnublated layers that contain units that fluctuate input into output.

The design tool developed by roboticists that can be utilized as a recipe for autonomous robotics system. The team has developed optimization code that can be used to simulate almost any autonomous robotic system and that can be used to automatically determine how and where to make changes to a system to improve a robot's performance. The researchers created a computer code or optimization framework that can automatically identify changes that can be made to an autonomous system in order to reach a desired result.

 It enhance production in smart factory settings and lower the cost of digital manufacturing for SMEs that couldn't previously afford turnkey systems. The transition to Industrie 4.0 relies on R&A technology as a fundamental building block. Robots used in collaborative environments help humans by automating repetitive and physically taxing chores. They no longer need cages and can be employed in a variety of applications in contemporary production facilities. Pick-and-place solutions are one typical cobot application area. Activities that involve human-robot collaboration allow for quick training and can be managed by platforms that enable robot deployment at the proper workstation and workload augmentation or reduction in response to shifting production demands.