What's the future of lab automation?

Lab automation has come a long way since the days of industrial robots, with new advancements increasing efficiency between systems and workers. Peter Harris, HighRes Biosolutions, discusses how far the sector has moved and what the future holds.

The use of automation and robotics in scientific research has been around for quite some time, starting in earnest in the 1950s and progressing from decade to decade since. For many years, it was driven by an increasing focus on high-throughput screening for new small molecule therapies – an application area that was well-suited for the robotic technology available during the period from the late 1970s to the early 2000s. During that period, the robots available for use in laboratory integrations were industrial robots, principally produced for manufacturing environments, with high payloads, forces, and requirements for guarding and safety. As such, they were best suited for repetitive, high-volume activities where a large monolithic installation would work.

From this historic base and thanks to some important technological advancements, much has evolved in a reasonably short period. Today, the core architecture used in the most advanced lab automation systems looks little like the large monolithic systems of the past. Rather, lab automation has become flexible, modular and mobile, enabling its use in a much broader range of environments and applications. Now, rather than being suited for only the highest volume and repetitive tasks, automation and robotics are making their way into numerous new scientific research areas, frequently working in conjunction with humans on workflows rather than being separated by substantial safety barriers.

Perhaps the most important technological advancement influencing this change is the development of collaborative robots. Unlike industrial robots, collaborative robots are equipped with a combination of sophisticated sensors and internal electromechanical design elements that make them safe to work alongside. Scientists can interact with them in close quarters without any guarding or separation requirements. This elimination of the need to separate people from robots has had sweeping implications on how laboratory automation is configured, and the range of workflows and places it can be deployed. For example, the guarding requirements of an industrial robot almost always require it to exist in a fixed location, surrounded by barriers to protect humans from its motion. With collaborative robots, all guarding can be removed, making it possible to both shrink the overall size of the system, and allow the entire system to be easily reconfigured as needed.

The ability to reconfigure without guarding has created a variety of key changes to the fundamental architecture used in laboratory automation. Today, automation can be done modularly. Using docks and carts (wheeled and mobile elements), systems can be assembled and reconfigured as needed without any additional fixed tooling or reconfiguration costs. For example, systems designed to perform a screening operation can be easily reconfigured with different imagers by simply undocking and docking a mobile cart. Today, the best automation systems can be easily adjusted to operate around user requirements.

Extending beyond the use of carts, docks and modular elements, the next major wave of innovation that will likely come in core architectural design will be autonomous mobility. Robots that are safe to work alongside scientists can also be put on mobile bases capable of moving themselves to various locations around a facility to perform different functions. In many ways, the developments that have taken place in the warehouse robotics space by companies such as Locus Robotics, Fetch and 6 River Systems have paved the way for autonomous mobile robotics in a wide range of other industries, including biopharmaceutical research. Collaborative robots can be placed onto mobile bases and moved around lab environments without safety issues, opening up a wide new range of potential application areas. In fact, autonomous mobility technology is no longer the barrier to having effective mobile robotic solutions in labs. Rather, the challenge lies in developing the applications for such robots and optimising the central software that controls them. Autonomous mobile robots in labs are not going to be on the scene in the immediate future, but they are closer than you might think. As they start to become available, expect the range of applications and scientific disciplines robots address to grow significantly. 

It is also important to note some key trends in software, as anyone familiar with deploying laboratory automation solutions will tell you that software is just as, or even more important than hardware in delivering effective solutions. Perhaps the most important current trend in the lab space is cross platform connectivity. Where historically a sophisticated lab would have a variety of isolated software systems such as sample management, automation scheduling and management and data analysis systems, today these platforms are increasingly connected to one another, creating a more holistic environment for the scientist. With the right automation package, it is currently possible to order an experiment from a sample management system, which then communicates directly to the automation system and captures data downstream in a data analysis package, tying the whole process together. This trend of convergence and connectivity is sure to continue, producing powerful efficiency gains and enabling the possibility of closed-loop experiment control and iteration.

Thanks to some key technological innovations, lab automation has come a long way in a reasonably short time. Today’s solutions are a great deal more sophisticated and powerful than those available even five years ago. Perhaps even more exciting is how much seems right around the corner. The next 10 years will almost certainly see another large wave of substantial advancements, rooted in scientists and robots working together, doing what each does best.