Cameras can be used by the AI system to help detect and recognize objects with a higher degree of accuracy and detail. By training a machine to be able to determine what object is being detected, the next steps that need to be taken to perform a task can be optimized quickly and reliably.
By using a variety of sensors, a robot can be trained to be able to detect what type of object is being worked on. This can be accomplished by detecting the physical properties of the object and relating its findings to other objects with similar properties. Typically, as machines will be learning continuously, databases with information will be used as a reference. This data will be discussed upon to a further degree.
Combining the power of the machine learning vision and grasping systems, the motions required to be made by the robot can be accomplished with precise levels of accuracy while adapting to its surroundings. The motion control aspect is accomplished with automatic motion devices, such as Progressive Automations linear actuators.
The most important variable in machine learning is the data collected by the AI system. Big data is used to compare, analyze and build the autonomous tasks performed by AI enabled robotics. All the information collected can and will be stored in databases specific to the variable being analyzed. The increase in data will be exponential as more robotics will be implemented in real-world applications – collecting more information and providing real-time feedback.
The use of electric linear actuators will only increase as more and more types of robotics are being implemented in autonomous systems in many different industries. Artificial Intelligence and machine learning will improve as time goes on and is set to improve our daily operations to a high level of optimization.