Whether it’s robots being used in an airplane factory to install fasteners, or ones that perform tasks in construction or space exploration, the use of robots is rising in these and many other industries. Despite sociopolitical and economic concerns, the trend is rapidly gaining momentum, and brings with it new ways to think about how we capture data, analyze it, and measure productivity.
Within a few years, as described in A Futuristic View of the 777 Fuselage Build, robots will fasten fuselage panels together for the Boeing 777 commercial airplane, rather than using humans to install some 60,000 fasteners.
In many cases, robots are teamed with humans, where humans intervene to ensure task efficiency. Take the construction industry. Autonomous Aerial Robots to Monitor Construction talks about unmanned aerial vehicles (UAVs) that fly over construction sites, capturing photos and videos of the operations at the site. Currently, operation of these UAVs very often relies on human intervention although, as described in the article, research is underway to make it computer-operated, with automated data collection and analysis. The hope is to monitor progress and adherence to construction plans, oversee the usage and location of equipment and workers, and track performance, in a less cost-prohibitive way. Less expensive monitoring can encourage more monitoring, and an increase in monitoring can lead to more complete data for subsequent analysis.
Measuring human performance is one thing. Measuring the performance of the robots themselves and analyzing the data generated from them are another. What types of data can be collected and analyzed?
One aspect to measure could be how efficient is it to have a machine do a task entirely versus having a person do so. How well does the robot do without any manual intervention from humans, and at what point does performance go down? By looking at robot telemetry, it’s possible to see whether everything goes as expected, or where along the way things break down.
Another example is gauging how well this team of humans and machines works together, while keeping in mind that each situation is different. Specifically, less human intervention might be a good thing in some cases, whereas in other situations it could be detrimental.
Take space reconnaissance, where a Lidar (Light Detection and Ranging) instrument shines very rapid pulses of light to determine, for example, how safe it is for vehicles (whether operated entirely by robots or not) to land on the moon. This instrument needs to be manually shut down/powered up by a human during times of high temperatures or it will overheat. In this case, the efficiency of this human-machine team might be measured, which could lead to ways to improve or adjust the level or method of automation.
Not only are robots automating certain operational tasks, they are also automating or improving the efficiency of data gathering and analysis from various sources.In my next post, I’ll show you what I mean by this. I’ll also talk about how the performance of machines can be measured (as opposed to measuring human performance).
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