Overview
The DroneNet project is research project sponsored by Tetfund for the purpose of development a network of drones with networking and intelligent capacity surveillance and monitoring. The network will be controlled from a central node from where important coordination and decision making can be done
Research methodology and approach
To effectively achieve the research and training objectives for the drone net project a mulit-facted and disciplinary approach will be used. A combination of development of a network of drones equipped with vision sensors that leverages AI-based computer vision for monitoring and event detection. This reflects the need to development in house skills across different fields that can help in bolstering aerial monitoring and security
Impact: It will have impact across social, economic, security and technological impact
The aim of this project is to develop a network of autonomous quadcopter UAVs capable of intelligently extracting events of interest, thereby demonstrating the effectiveness of such platforms for significantly improving situational awareness in Nigerian cities towards security, environmental monitoring, and intelligent transportation system applications.
Research Objectives:
Aims and Specific Objectives of the Research Project
The aim of this project is to develop a network of autonomous quadcopter UAVs capable of intelligently extracting events of interest, thereby demonstrating the effectiveness of such platforms for significantly improving situational awareness in Nigerian cities towards security, environmental monitoring, and intelligent transportation system applications.
The objectives of the project are to:
⦁ Develop a quadcopter UAV with telemetric capabilities;
⦁ Develop an automatic landing and recharging system (ALRS) for the quadcopter in (i);
⦁ Integrate the ALRS in (ii) into a control node containing a computational unit;
⦁ Develop a network of up to four control nodes each capable of controlling one unit of the quadcopter in (i) ;
⦁ Develop monitoring and artificial intelligence-based control software for automatically extracting events or patterns of interest from video footage captured by the quadcopters; and
⦁ Test the combined system in (iv) and (v) at a scale covering up to 5% of a selected Nigerian city for security, environmental monitoring, and intelligent transportation use cases.
Training Objectives:
⦁ At least two academic staff mentored.
⦁ At least two postgraduate students and 10 undergraduate students trained