In the simplest of terms, machine vision is the ability of a machine to see. Therefore, in the same way that a human being extracts information from what they see, a machine vision enabled machine should extract relevant information from the objects it sees.

Machine vision, just like machine learning and cloud computing, is a growing technology. It is expected that by 2022, the machine vision market will be worth 15.46 US dollars especially because the past couple of years have seen research and investment on machine vision increase.

Some of this research has been centered on the benefits that could be attained by mass incorporation of machine vision into robotics. In response to this, at the AIA vision show this year, one of the sessions focused on how machine vision was enabling or influencing collaborative robots.

Speakers at show identified ways that machine vision was benefiting collaborative robots and ways that further incorporation would benefits cobots in the future.

1. Make Future Collaborative Robots Safer

One of the biggest selling points of collaborative robots is that they are safe. Collaborative industrial robot manufacturers have done their very best to ensure that cobots can work alongside human beings without causing damage. Their work has been successful and this is part of the reason the robotics industry is experiencing massive growth.

However, it is crucial to note that, collaborative robots are not one hundred percent safe. Leading manufacturers in the market often caution clients that before installation an assessment to determine the risk must be carried out. If the analysis proves that installing the robots would be too risky, then another option is advised.

Here is a sample scenario, a drilling robot is designed with slow motion and sensors to avoid harming humans. However, in a situation where the human worker gets in the way of the robot unconsciously, the robot’s end effector (the actual drill) can harm that worker gravely. Similarly, with a pick and place robot, the actual load which is usually quite heavy, can harm a human employee in case of an accident.

To avoid such scenarios, speakers demonstrated how a robot outfitted with 3D cameras and applying high-level machine vision can help collaborative robots avoid contact with human workers. In addition, if machine learning was also incorporated, then the robots could identify a potentially dangerous scenario and avoid it.

Video cameras were also identified as a way to monitor the work environment and improve safety.

2. Increase the Efficiency of Cobot Arms

A leading cobot manufacturer demonstrated how when 2D cameras were paired with collaborative robot arms, efficiency was increased. In the case of a pick and place cobot, for instance, the cobot guaranteed more accuracy and faster completion of tasks.

Incorporation of 3D cameras, on the other hand, made robots suitable for high accuracy tasks such as inspection and bin picking.

Such applications are already in existence today as demonstrated, but as technology grows and improves it is expected that machine vision will ensure the efficiency is even higher and that robots users will get maximum value from their collaborative robots.

3. Provide Greater Insights and Control

Machine vision can be used to provide greater insight and help in decision making. In the case of precision agriculture, for instance, machine vision can be employed to guarantee optimized returns.

In the case of military service robots, though machine vision is already being used by drones, technological advancements in machine vision will improve the accuracy. The greater the accuracy, the clearer the insight and this, in turn, will ensure better decision making.


The above three impacts of machine vision on collaborative robots barely scratch the surface of what machine vision is capable of bringing to the table. They simply enumerate the pressing issues identified by experts at the AIA vision show.

However, with increasing research, applications that marry cobots and machine vision are set to increase.



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