AI and machine learning are frequently used to improve robots

Artificial Intelligence

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29 Dec 2021
7 Min read

Role Of AI In Robotics And Manufacturing Automation

Artificial intelligence (AI) and machine learning (a subset of AI) are transforming practically every industry and enhancing the capabilities of standard technology.

AI and machine learning are frequently used to improve robots. In the twenty-first century, robotics and manufacturing automation have the potential to transform and disrupt the global economy. The widespread usage of AI-powered robots in manufacturing and warehouses should result in significant advances in productivity and efficiency.

However, these potentially game-changing AI robotics breakthroughs are contingent on the availability of high-quality robotics training data. Developers need pixel-perfect, correctly labelled photos and movies to create new generations of Artificial Intelligence (AI), facilitating machine learning and allowing robots to work efficiently in dynamic industries. Professional data annotation services, such as Keymakr provide picture annotation for automated machines to address this need.

Here are a few reasons why an AI robot might be better than people who don’t have it.

Aware Of Surrounding With AI-Enabled Machines

In the industrial sector, robots can assist businesses in completing more tasks with fewer errors. Of course, when introducing robots into the workplace, safety is paramount, which is why several AI robotics companies are working on solutions that allow robots to recognise their surroundings and respond appropriately. Because of this, robots can no longer be kept in cages, but human safety remains paramount.

AI-enabled autonomous mobile robots (AMRs) can also understand the layout of a warehouse and drive safely around warehouse obstacles in real-time. These vehicles move parts and finished goods, sparing humans from a job that would ordinarily require thousands of steps every day.

Robotics And Manufacturing Automation Can Learn From Their Mistakes Using ML

Thus with their experiences, individuals gain knowledge. Thanks to technological advancements such as machine learning, robotics applications may be able to achieve the same thing. They may not require continual, time-consuming human training if this occurs. Learning would instead happen as a result of ongoing use.

Researchers at the University of Leeds are developing a robot that utilises artificial intelligence to learn from its mistakes and assesses its data over time to make better decisions.

Approximately 10,000 trial and error attempts are used to train the bot, allowing it to discover which strategies are most likely to succeed. According to Australian researchers, machine learning was also used to train humanoid robots how to adapt to unexpected changes in their environment. According to simulations, the biped robot could stay stable on a moving platform thanks to the machine learning system.

Robotics and manufacturing automation shortly may be more adaptive thanks to machine learning applications like these. If that’s the case, they’ll be more desirable to businesses looking for robots to do tasks or work in surroundings with a lot of variation.

How Machine Learning Is Helping To Advance Robotics?

Thanks to machine learning, robots and automated production rapidly gain expertise and capacity. Large, flexible training datasets have resulted in significant gains in a variety of areas:

Safety: The safety of automated workstations is steadily improving because of machine learning in robots. Industrial robots’ environmental awareness has been enhanced using two and three-dimensional image collections. A fast and reliable object detection system will let these machines avoid obstacles and humans.

Quality: Robotics with excellent image labelling improve machines’ capacity to detect errors and problems in products just off the manufacturing line. Cameras using computer vision can detect issues that are not evident to the naked eye. Furthermore, AI-assisted inspections can be performed more often without lowering detection rates.

Longevity: Artificial intelligence systems are also being used to maintain other equipment and structures. Models are being trained to recognise probable mechanical faults or malfunctioning machinery before a catastrophic failure using visual datasets containing photos precisely tagged with examples of wear and tear. This type of proactive AI surveillance has the potential to extend the life of many essential pieces of equipment.

Industries and Applications

The breakthroughs in robotics brought about by AI and machine learning have been seen across many industries. Many factories and workplaces today have AI-driven machines that play an essential role:

Warehouses and distribution: Visual datasets have trained robotic arms as pickers in distribution warehouses. This will boost the rate at which packages can be moved through a distribution centre.

Agriculture: Robotic arms could be used as fruit pickers in the future. These machines can harvest ripe produce indefinitely thanks to object detection picture training. Agricultural technology is constantly improving because of video labelling in robotics training datasets.

Automobile Manufacturing: Bounding boxes are used by robots in automobile companies to identify cars as they move down the assembly line. In a congested manufacturing environment, they should avoid potentially costly collisions.

Logistics: Artificial Intelligence (AI) technologies improve warehouse efficiency by detecting stock misplacements and inefficient space usage. Annotated storage photos can be used to train warehouse cameras.

Garbage Management: The waste management sector is increasingly turning to machine learning-assisted robots. These robots are programmed with thoroughly compiled and annotated datasets that allow them to discriminate between different sorts of waste and properly dispose of it. As a result, humans and hazardous trash can be kept separate.

Conclusion

The AI revolution in robotics and manufacturing automation is centred on image annotation for robotics. The speed with which these technologies are altering industries might be disorienting, but machine learning’s potential applications and advantages are evident.

New generations of AI must have access to the highest quality training data at the scales required. To develop precise and cheap datasets, Keymakr employs professionally managed teams of expert annotators. To schedule your demo, please get in touch with a member of our staff.

For more information on how artificial intelligence (AI) can grow your business, contact the ONPASSIVE team.

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