Trying out different behaviours is one of the classic learning methods. Success or failure decides which behaviour is adopted. This principle can be transferred to the world of robots.

Machine vision processes have become standard practice in quality assurance. Inspecting reflective surfaces, however, presents a challenge. A technology known as deflectometry can be used to reliably detect all types of defect even in these circumstances.

In the past, the amount of processing power necessary to perform color-based machine vision applications was often an insurmountable hurdle. Even when manufacturers did offer color vision, they would typically convert images to grayscale prior to analysis—a strategy that significantly reduces precision and fails to detect edges defined by similar colors.

Lighting and lighting control is a critical component of any machine vision system since it has a massive influence on the signal to noise ratio and contrast in the images acquired.

Sensors that report a factory’s performance by measuring machine functioning, downtime, and production goals are in high demand.

Thermal imaging can be used for quality control in many industries. It is a nondestructive inspection method, which is especially used to detect flaws that are not visible on the surface. 

For decades, many manufacturers have counted on robots to tirelessly produce parts of predictable quality. One of the key attributes of robots is their repeatability, which means that their tool tip will return to the same pre-programmed location with a known and relatively small error.