Lab Automation
rapid movement, and building a robotic line requires quite a bit of forethought about how to make sure the robots and people stay separate. In addition to substantial guarding requirements, the traditional industrial robot line requires signifi- cant upfront programming and configuration in order to be effective. Each robot needs to be taught its positions and movements, with requisite and often complex work holding systems to ensure the workpieces are in the correct location and that the robot does not accidentally crash. A traditional industrial robot requires very high overall design rigidity in order to have repeatability. As a result, installing a traditional industrial robot line is usual- ly very costly and inflexible. The robots themselves are a fraction of the total line cost. Moreover, if after installation the user decides they want to switch the design or configuration of the product being made on the line, the changes to the configu- ration can be time-consuming and expensive as the systems have a certain monolithic character to it, lacking flexibility and ease of adjustment. As a result of the above limitations and characteristics of traditional industrial robots, the application of robot technology has generally had a shared set of requirements: high volume, repetitive tasks unlikely to change for a long enough period to generate a return on investment. For example, in 2015 almost 70% of all industrial robots were used in the com- bination of automotive, electronics and metal man- ufacturing industries3, nearly all of which have these characteristics.
Robot factory
While the above comments likely seem obvious when thinking about an automotive assembly line, in truth almost all other robot deployments have effectively been extensions of the basic high-vol- ume assembly line model, including in drug discov- ery. Automation of scientific research tasks has found its strongest purchase in applications such as high-throughput screening (HTS) and compound management, where like typical industrial assem- bly lines, repetitive high-volume workflows are common. Traditionally these systems have used scaled-down versions of traditional industrial robots, also requiring significant guarding, increas- ing the overall footprint and limiting the flexibility and ease of interaction between robots and humans. Because these systems are not able to allow the robot and the human to work together, they must be totally automated – all tasks in the workflow must be done without human interven- tion even if a human is more effective than the robot at certain steps or processes. While these sys- tems have been effective in increasing research pro- ductivity, like the automotive assembly line they have not penetrated broadly into other areas of the drug discovery process such as chemistry and cell culture, due to the nature of the workflows in those areas.
Collaborative robots Today, the limits of traditional industrial robots are being pushed aside by a new generation of robots known as collaborative robots, or ‘cobots’
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Drug Discovery World Fall 2017
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