Self-adjusted AI Robot

The AI system couldn’t learn as human being, but developers could make it self-adjusted based on a set of live data and measurement.

There are many robots used in manufacturing plants, e.g. auto parts, paper clip, binders, etc. Those robots were pre-programmed to create a product according to its specification.

The issue was robot could be out of sync or miss calibration over time of use. The developers could estimate or predict the out of calibration cases and use sensors to measure real time products in order to adjust the robot automatically.

One of the issue was the conveyor belt is limited in length. The robots made parts and dropped it on the conveyor belt, which run at determined speed. Some parts had more minor defects then others, thus time required to fix it longer. For example, auto parts made by robots had something called “gates or flashes”, which required operator to trim it manually with a knife or a cutter. If a part had more gates or flashes, operators would need more time to trim those. However the robot didn’t slow down, thus many parts would be lined up on the conveyors belt. The robot may be halted in this case, and time to restart a robot is long; the products right after starting robot is not good, i.e. wasted materials.  The robot should be equipped with a sensor or camera to monitor parts on the conveyor belt. It should slow down if there were many parts on it.

There are many cases, developers could implement a system equipped with sensors, camera, or laser beam to monitor a situation and re-calibrate the robot according to live data and measurement. For example, if a part is longer than specification, it could adjust the robot to make next part correctly in length.