The leadPI of DroneITS, Dr. Hamid Menouar, was invited to present the activities and outcomes of the project at a workshop on “Mobile Crowdsourcing for Qatar Smart City” that was hosted today by #QatarUniversity. Dr. Hamid was also honored to moderate in the same occasion an interesting panel discussion, by distinguished and high caliber researchers, on the future of transportation with a focus on drone delivery services.
Here are some of the main takeaways:
Dr Hamid Menouar, the LeadPI of the DroneITS project, was invited at the Eighth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2019), June 30, 2019 to July 04, 2019 – Rome, Italy, to deliver a keynote speech entitled “Unmanned Aerial Vehicles as an Enabler for Next Generation Mobility”, which presents the activities and the outcomes of the DroneITS project.
A showcase of our end-to-end flight automation including precise landing by combining GPS and Computer.
Automation of landing on a small surface is necessary in many drone-based applications, and it is one of the most challenging parts for achieving a fully automated flight.
What if we let drones/UAVs to ride land #transport (buses) to extend their batteries lifetime i.e. their missions coverage in terms of time and space? This is the idea we have elaborated in our article entitled “Exploiting Land Transport to Improve the UAV’s Performances for Longer Mission Coverage in Smart Cities” that has been accepted for publication and presentation at the upcoming 89th edition of the IEEE Vehicular Technology Conference VTC2019-Spring that will be hold on 28 April – 1 May 2019 in Kuala Lumpur, Malaysia. This work is an outcome from our NPRP project DroneITS which is funded by the Qatar National Research Fund (QNRF).
We have been invited by QNRF to attend the QNRF Research Outcomes Seminar (ROS) and present the outcomes of our NPRP DroneITS project and their potential to serve as a base for supporting sport-related applications.
It is one of the most exciting phases in any R&D project, the start of the development of the prototype that will be used to proof and demonstrate the findings of the project. In DroneITS project, we have planned for this activity to start as soon as possible (just a year after the kickoff of the project), to make sure we have a reliable and a stable test-bed before the closure of the project, by end of 2019.
Below is a photo of two colleagues (Miss. Nour Alsahan and Mr. Ahmed Abuzrara) working on the development and integration of the first DroneITS’s UAV, and the work is progressing very well. We hope to have a first version of the prototype ready for a demonstration in a month.
Our paper entitled “On the Placement of UAV Docking Stations for Future Intelligent Transportation Systems” has been presented by Dr. Hamid Menouar at the workshop on Positioning Solutions for Cooperative Intelligent Transportation Systems which was hold in conjunction with the IEEE Vehicular Technology Conference (VTC-Spring 2017), Sydney, Australia, June, 2017 (http://www.ieeevtc.org/vtc2017spring/).
The abstract of the presented paper is below:
Unmanned Aerial Vehicles (UAV) have attracted a lot of attention in a variety of fields especially in intelligent transportation systems (ITS). They constitute an innovative mean to support existing technologies to control road traffic and monitor incidents. Due to their energy-limited capacity, UAVs are employed for temporary missions and, during idle periods, they are placed in stations where they can replenish their batteries. In this paper, the problem of UAV docking station placement for ITS is investigated. This constitutes the first step in managing UAV-assisted ITS. The objective is to determine the best locations for a given number of docking stations that the operator aims to install in a large geographical area. Based on average road network statistics, two essential conditions are imposed in making the placement decision: i) the UAV has to reach the incident location in a reasonable time, ii) there is no risk of UAV’s battery failure during the mission. Two algorithms, namely a penalized weighted k-means algorithm and the particle swarm optimization algorithm, are proposed. Results show that both algorithms achieve close coverage efficiency in spite of their different conceptual constructions.