Interview with Researcher Wael Mohammed, Tampere University
The AI-PRISM team has officially started the development of its Alliance of Open-Access Pilot Suite led by partner Tampere University (TAU). Our goal is to showcase a human-centric AI-based ecosystem that focuses on manufacturing scenarios. This will be achieved by offering an end-user simulation platform.
The platform allows organizations to remotely access their facilities, equipment and manipulators established within the alliance. The AI-PRISM software suite will facilitate the platform, which includes an interactive training engine and supports scheduling and optimisation. We will provide solutions for developers in SMEs and start-ups to openly access our robotic systems.
Today we have the pleasure to meet Wael Mohammed Researcher from the TAU team working on the matching and scheduling engine, one of the Open-Access Pilot Suite tools that engage academic and industrial actors, using the built-in AI- PRISM simulation services to empower demonstration, training, certification and development activities.
Dr Wael, thanks for being here with us!
Please introduce yourself and tell us about your work on this tool within this Open-Access Pilot Suite
Hello, thank you for having me. As TAU leads work package 7, I am working with the consortium members to design the development of the open-access pilots’ platform.
What is the tool about?
This tool, the matching and scheduling engine, will help the end-users to find the most suitable pilot with all the necessary support. You can think of it as an “Airbnb” for robotics pilots. The difference is that in an Airbnb rent, you don’t need to be trained on using the shower or certified to cook something. In AI-PRISM, the concern is to keep the safety and privacy at a very high level. So, the matching process will include more aspects to it once an end-user decides to access a pilot.
How will this engine’s interface look like?
Well, as with many matching services, the end user must have an active and authenticated profile. Then, the user will be prompted to provide certain parameters for the algorithm that will optimize the schedules. Then, after the algorithm ends, the user will be presented with choices with some hints to help in choosing the most suitable pilot.
What are the main challenges of developing this engine?
There are many variables in such a problem and these algorithms must provide the scheduling fast enough for the user and must take into account the pilot owner’s schedule as well. So, it is not a simple optimization problem.
How do you tackle them?
The advantage that we have in this project is the diversity of the team with many disciplines that allows sharing the work between different partners. So open collaboration and communication between partners is the solution.
Why will this tool be relevant to the manufacturing industry and its workers or end-users?
As said before, this engine is needed for managing eh access of the pilots to guarantee that safety, privacy and security are at a high level while maximizing the experience of both the pilot user and pilot owner.
Thanks for your time and see you soon for the following steps!
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