General Objetive

The main goal of this project is to develop affordable and replicable mobile robotic systems to promote smart COVID-19 environments with monitoring, disinfection planning and control capabilities of respiratory pathogens related to COVID-19.

Specific Objectives

1. Establish an international cooperation network for deploying mobile robotic platforms and monitoring systems in different countries, as well as for sharing technological and research advances from the network partners.
2. Implement robotic systems for planning processes of disinfection and decontamination of outdoor and indoor environments.
3. Develop control strategies based on artificial intelligence algorithms to detect non-safety and hazardous conditions related to human behaviors.
4. Perform laboratory validations of the proposed systems for their implementation in real scenarios.

PHASE 3: NON-SAFETY AND HAZARDOUS CONDITIONS DETECTION

In addition to performing disinfection processes to prevent the transmission of COVID-19, it is necessary to abide by different biosafety protocols. Among these are the non-agglomeration of people and compliance with rules of social distancing in areas where there is a high risk of contagion. Hence, this phase is aimed at developing AI and machine learning based strategies to detect non-safety and hazardous conditions related to human behaviors.

PHASE 4: EXPERIMENTAL VALIDATION OF THE SYSTEM

The performance of the developed modules will be validated under controlled conditions.

EXPECTED OUTCOMES AND IMPACTS

To guarantee reproducibility, the main outcomes of this project will be shared as open-source. Programming environments such as Python, C++ and ROS will be used, reaching a large community worldwide. Several technical outputs will be tackled:

1. Robotic Cloud Server: This server will allow the interconnection of robotic platforms at the multiple laboratories of the network partners, as well as provide a highly capable processing machine that allows the execution of multiple complex algorithms.

2. Planification and navigation strategies for disinfection: It is an interesting research challenge to determine the frequency at which disinfection processes should be executed. Moreover, the navigation strategies will provide optimal routes for large areas coverage, guaranteeing maximal efficiency. This system will impact the community, through the documentation and publication of these algorithms and disinfection programs.

3. Outdoor surveillance system: This system will monitor the physical distance and people conglomeration in open spaces through video data provided by a drone. Although similar solutions have been reported, this system will reduce the human input to remote control tasks, leaving the detection and analysis of people's behaviors to the robotic system.

4. People control system: An indoor monitoring system to detect hazardous behaviors of humans will be developed. This system will guarantee the execution of daily living routines, while preventing the contagion of COVID-19.

5. Bio-Hazards Detection System: This system will provide facial recognition features to estimate the proper use of masks, as well as a temperature control module capable of determining potentially infected people.

6. Smart Rooms: This project will provide an intelligent module capable of storing, reporting, and updating the disinfection status of any indoor environment. It will be highly adaptable to provide disinfection monitoring in real scenarios.

These outputs will be supported by the experience of the network collaborators, and will be evaluated under laboratory conditions. At least two conferences or journal papers will be submitted to disseminate these results in the scientific community.

This project will configure an international cooperation network to generate a wider impact, which is led by the Colombian School of Engineering Julio Garavito, with leading Investigator Carlos A. Cifuentes and the University of Edinburgh support by Professor Subramanian Ramamoorthy along with researchers and clinicians from Latin America (Colombia, Brazil, Argentina and Chile). The outcomes will be validated under laboratory conditions and will be deployed as an open-source platform to ease external collaborations.

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