ai-learning-artificial-intelligence-conc

Tampere

 

Automatic Capability Matchmaking for

Re-configurable Robotics Platform

(Eeva Järvenpää & Minna Lanz @ TAU)

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NEED for AI:

Currently, designers find feasible resource solutions by comparing the characteristics of the product to the technical properties of the available resources browsing through online catalogues to select thousands of components manually.

Using new computer-aided intelligent planning methods and tools, the time and effort put into system design can be reduced drastically.

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AI REGIO SOLUTION:

An automatic capability matchmaking system via:

  • OWL-based models, which describe resources, their capabilities, interfaces, and product requirements 

  • SPIN rules, which are defined to compare the product requirements against resource capabilities to find feasible resource alternatives from various catalogues, will be demonstrate

A web service interface, through which the designer may run the matchmaking requests and receive the results.

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EXPECTED BENEFITS:

  • reduced the time for system design and reconfiguration planning

  • reduced design flows and errors

  • more alternative resources and configurations to be considered (possibly leading to more innovative solutions)

TAMPERE region DIH experiment is focused on the demonstration of an automated capability matchmaking system to facilitate rapid design and (re-)configuration of a reconfigurable robotics platform. It will be done by running different matchmaking scenarios to automatically find alternative resources and resource combinations to the defined product requirements.