|Customer :||H2020 VOJEXT|
JinZone is a project that aims to create a service capable of supporting robots in manufacturing, in the mapping, localisation and navigation of spaces. It combines established artificial intelligence techniques with a proprietary reasoner and orchestrator layer developed by Elif Lab, which exploits the Freudian mental model and its proprietary mathematical and algorithmic formalisation to create a new AI paradigm.
In this way, Jinzone retains the advantages of known navigation technologies, but can exploit an additional layer of abstraction, reasoning and control that operates – like the mind model – on abstract representations. This adaptive technology allows for better task orientation without costly retraining, the ability to operate in dynamic and chaotic environments, and greater control over all phases. Decisions made by the robot are transparent and can be evaluated and redirected. In addition, JinZone makes it easier than other technologies to operate in unknown or unmapped environments, reducing the time to introduce robots into new contexts.
▪ SIFT (Scale-invariant feature transform) algorithm for object reconstruction from images.
▪ A* algorithm for optimal path calculation and programming
▪ Elif Lab’s proprietary reasoner and controller, capable of harmoniously handling information from movements and external stimuli and dynamically and adaptively generating sequences of actions to achieve the desired result
▪ Semantic component that enables transparent communication of robot choices and allows the robot to absorb operator and user inputs
▪ MQTT protocol
JinZone has been selected in the VOJEXT open call and is currently funded in the context of the European Horizon 2020 research and innovation programme, grant agreement No 952197.