Robotics is rapidly advancing from science fiction to practical uses across industries like manufacturing, logistics, and healthcare. Key trends include autonomous systems operating independently, robots collaborating with humans for improved productivity and safety, and AI integration that allows robots to learn and adapt. This technology enables both large enterprises and SMEs to optimize processes and meet growing demands.

As businesses increasingly rely on machine vision to enhance quality, improve productivity, and increase the bottom line, technology providers are relying more on industrial computing solutions that enable faster processing speeds and higher efficiency, or that support new tasks altogether. 

As consumers demand higher-quality goods at steadily increasing volumes, the chief benefits of industrial automation — speed, accuracy, and consistency—become more important to businesses worldwide with each passing day. 

The first part of this series covered the basic principles that make high-accuracy measurements possible for machine vision. The second part of the series looked at challenges to accurate measurements due to the application conditions. This third part looks at characteristics and components of the vision system that can limit the achievable accuracy.

Initially seen as science fiction, machine vision in manufacturing faced hesitance due to high costs and lack of awareness. However, interest has surged, shifting the focus from "Can it be done?" to "How will we do it?" This reflects significant transformative changes in the industry.

Automated processes are vital in industrial production, with robots handling finished products and sorting parts for quality assurance. Equipped with 3D cameras and machine vision, they accurately identify and grasp items from disordered bins.

The first part of this three-part series covered the principles that allow machine vision to make high-accuracy measurements. This second part examines challenges that the measurement environment imposes on machine vision and gives approaches to mitigate the effects and retain much of the high-accuracy capability.

Machine vision can measure with greater precision and accuracy than human vision. This series starts by exploring techniques for high-precision measurements in vision systems. The next installments will examine challenges and solutions for maintaining this precision and accuracy. First, we'll clarify key terms related to measurement accuracy.

Change is inevitable, and our greatest tool to deal with it is adaptability.