How AI and Machine Learning Can Improve Robotics and Manufacturing Automation

May 11, 2021

“AI, robotics and other forms of smart automation have the potential to bring great economic benefits, contributing up to $15 trillion to global GDP by 2030.”

  • PriceWaterHouseCooper report, How Will Automation Impact Jobs

Robotics and manufacturing automation has the potential to transform and disrupt the global economy in the coming century. The widespread use of robots powered by Artificial Intelligence (AI) in manufacturing and warehouses should mean huge increases in productivity and efficiency.

However, these potentially transformative AI robotics developments are reliant on the availability of excellent training data for robotics. In order to iterate new generations of AI, developers require pixel-perfect, accurately labeled images and videos that will facilitate machine learning and allow robots to operate effectively in dynamic industries. In order to meet this need, professional data annotation services, like Keymakr, are providing image annotation for automated machines.

Training data for robotics | Keymakr

How Machine Learning is Pushing Robotics Forward

Machine learning is rapidly increasing the competency and capacity of robotics and automated manufacturing. Large, flexible training datasets have led to marked improvements in a number of areas:

  • Safety: Machine learning in robotics is steadily improving the safety of automated workspaces. Two and three dimensional image datasets have been used to enhance the environmental perception of industrial robots. Fast, reliable object detection will ensure that these powerful machines will avoid obstacles and human beings.
  • Quality: Excellent image labeling in robotics is increasing the ability of machines to identify defects and faults in products fresh from the assembly line. Computer vision enabled cameras can spot defects that are invisible to the human eye. In addition, AI controlled inspections can be carried out more frequently without a drop in detection rates.
  • Longevity: AI systems are also being deployed for the maintenance of other machines and structures. Visual datasets featuring images accurately labeled with examples of wear and tear are being used to train models to spot potential mechanical problems or defective machinery before a catastrophic failure. This kind of preventative AI surveillance can extend the life of many vital pieces of equipment.
Image annotation for robotics | Keymakr

Industries and Use Cases

The advancements precipitated by AI and machine learning for robotics have been seen in a wide variety of industries. There are now many factories and workplaces in which AI driven machines are playing a vital role:

  • Warehouses and distribution: Robotic arms, trained with visual datasets, are now able to act as pickers in distribution warehouses. This will increase the speed at which packages can be moved through a centre.
  • Agriculture: Robotic arms also have the potential to be deployed as fruit pickers. Object detection image training enables these machines to tirelessly pluck ripe produce. Video labeling in robotics training datasets is continually refining agricultural tech.
  • Car Manufacturing: Robots in automobile factories are using bounding boxes to identify cars as they move down the assembly line. This should enable them to avoid potentially costly collisions in a crowded manufacturing environment.
  • Logistics: AI systems are increasing the efficiency of warehouses by identifying misplacements of stock and suboptimal space usage. Warehouse cameras can be trained using annotated storage images.
  • Waste Management: Increasingly the waste management industry is choosing to deploy robots backed by machine learning. These machines are trained with meticulously collated and annotated datasets that allow the robot to distinguish between types of waste and dispose of it correctly. This allows humans and dangerous waste to remain separate.
Keymakr Demo

Professional Data Annotation Services Help Advance Robotics

Image annotation for robotics is at the core of the AI revolution in robotics and manufacturing. The pace at which these technologies are transforming industries can be dizzying, but the potential applications and benefits of machine learning are clear.

It is essential that new generations of AI have access to the best quality of training data at the scales required. Keymakr uses professionally managed teams of experienced annotators to create datasets that are precise and affordable. Contact a team member to book your personalized demo today.

Inna Nomerovska

Inna Nomerovska is the VP of Marketing and Brand Strategy at Keymakr | Keylabs. She is a tireless technology enthusiast with 15 years of experience in international marketing and startup background.

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