Applying Artificial Intelligence and Machine Learning in Robotics

Artificial Intelligence and Machine LearningArtificial intelligence (AI) is set to disrupt practically every industry imaginable, and industrial robotics is no different. The powerful combination of robotics and AI or machine learning is opening the door to entirely new automation possibilities.

Currently, artificial intelligence and machine learning are being applied in limited ways and enhancing the capabilities of industrial robotic systems. We have yet to reach the full potential of robotics and machine learning, but current applications are promising.

4 Tenets of Artificial intelligence and Machine Learning in Robotics

There are four areas of robotic processes that AI and machine learning are impacting to make current applications more efficient and profitable. The scope of AI in robotics includes:

  1. Vision – AI is helping robots detect items they’ve never seen before and recognize objects with far greater detail.
  2. Grasping – robots are also grasping items they’ve never seen before with AI and machine learning helping them determine the best position and orientation to grasp an object.
  3. Motion Control – machine learning helps robots with dynamic interaction and obstacle avoidance to maintain productivity.
  4. Data – AI and machine learning both help robots understand physical and logistical data patterns to be proactive and act accordingly.

AI and machine learning are still in their infancy in regards to robotic applications, but they’re already having an important impact.

Two Types of Industrial Robot Applications Using Artificial Intelligence and Machine Learning

Supply chain and logistics applications are seeing some of the first implementations of AI and machine learning in robotics.

In one example, a robotic arm is responsible for handling frozen cases of food that are covered in frost. The frost causes the shape of the objects to change – the robot is not just presented different parts occasionally, it’s being continuously presented with differently shaped parts. AI helps the robot detect and grasp these objects despite the variations in shape.

Another prime example of machine learning involves picking and placing over 90,000 different part types in a warehouse. This volume of part types wouldn’t be profitable to automate without machine learning, but now engineers can regularly feed robots images of new parts and the robot can then successfully grasp these part types.

AI and machine learning will have a transformative impact on industrial robots. While these technologies are still in their infancy, they will continue to push the boundaries of what’s possible with industrial robotic automation over the next few decades.