European project ENDORSE

Program: H2020-SC1-FA-DTS-2018-2

Title: ENDORSE - Safe, Efficient and Integrated Indoor Robotic Fleet for Logistic Applications in Healthcare and Commercial Spaces

Project duration: 36 months

Project Summary: The commercial exploitation of robotics for logistics operations has a bright future ahead of it and is rightly considered the next major deployment area. Though currently little used because of heavy financial constraints (networks of peripheral sensors, cartography, etc.) and existing IT installations, logistics robotics nevertheless benefits from new construction laws favoring large navigation areas and next generation telecom infrastructure. It is currently limited to the transfer of goods. The goal of ENDORSE is to go further in providing support, especially in hospitals, and to address the aforementioned technical barriers through knowledge transfer, multidisciplinary research and fruitful exchanges between universities and industry. One of the robots will be equipped with an electronic diagnostic support module (equipped with non-invasive sensors / devices and interfacing with the patient's medical history) and will serve as a mobile diagnostic support station. Four innovation pillars will be pursued:

• Infrastructure-less multi-robot navigation, i.e. minimum (if any) installation of sensors and communications buses inside the building for the localization of robots, targets and docking stations.
• Advanced HRI (Human Robot Interaction) for resolving deadlocks and achieving efficient sharing of space resources in crowded spaces.
• Deployment of the ENDORSE software as a cloud-based service facilitating its integration with corporate software solutions such as ERP, CRM, etc...
• Reconfigurable and modular hardware architectures so that diverse modules can be easily swapped. The latter will be demonstrated and validated by the integration of an e-diagnostic support module (equipped with non-invasive sensors/devices) and the Electronic Health Records (EHR).

Partners:

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 823887