## 5 Conclusions  
The potential for introducing mobile collaborative robotic platforms in the biomanufacturing sector in general, and in the personalised therapeutic sector in particular, was investigated in this paper. The applications that were primarily identified are related to the transportation and handling of equipment and devices as well as environmental sampling and monitoring. Automating these tasks will lead to lower contamination risks, manufacturing costs, and product prices, considering that labour costs account for up to 30% of production costs [104], while allowing microbiologists and operators to carry out more complex operations or to focus on process design and optimisation tasks.  
A collaborative mobile robot was used to carry out a series of different tasks, such as manipulation and transport of centrifugal tubes, manipulation of deformable medical fluid bags and manipulation of Petri dishes for scanning the QR code. The robotic platform was able to complete all these tasks successfully.  
Applications, such as the ones discussed in this paper will need to be validated through more testing. These tests can be carried out initially in biomanufacturing research facilities like the National Institute for Bioprocessing Research and Training (NIBRT) [105]. These tests will involve using the mobile robot to transport and manipulate the actual equipment used for production and verify the success rate, task completion time and contamination reduction. The solution can then be further tested in an actual production plant for final validation.  
The bioprocessing industry is at a crossroads where the level of automation adoption could shape the industry's future. For this to become a reality, a series of challenges related to the programming, commissioning and operation of robots will have to be overcome. Robots should be capable of exhibiting flexibility and versatility in a very conservative, risk-averse industry, and it will have to be demonstrated that they may indeed increase product safety and production throughput in a cost-effective manner. More flexible automation may lead to faster, cost-efficient personalisation of biomanufacturing products.  
**Author Contributions:** Conceptualisation, N.P. and R.M. ([PERSON]); methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--review and editing [PERSON]. ([PERSON]), [PERSON], [PERSON] and N.P.; visualisation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); supervision, N.P.; project administration, N.P.; funding acquisition, N.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Enterprise Ireland funded project MARVIN under project grant IP/2017/0671/A, co-funded by the Irish Government and the European Union, as well as by PM Group Limited, Lonza Walkersville Inc. and Novartis Pharma AG.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to thank the National Institute for Bioprocessing Research & Training for helping us conduct this study and for arranging discussions with experts from the biomanufacturing sector. We thank [PERSON] from University College Dublin for helping us to design and prototype the equipment used in this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.