What is Dronemap Planner? Dronemap Planner is cloud-based robots and drones management system that allows to remotely control and monitor robots/drones over the Internet. Dronemap Planner represents an realization of a cloud-robotics system that integrates robots/drones into the Internet-of-Things (IoT) and clouds. Dronemap Planner is an effective system to develop applications for drones/robots for the IoT and clouds. We present a detailed overview of Dronemap Planner in the following tutorial pages.
Low-cost Unmanned Aerial Vehicles (UAVs), also known as drones, are increasingly gaining interest for enabling novel commercial and civil Internet-of-Things (IoT) applications. However, there are still open challenges that restrain their real-world deployment. First, drones typically have limited wireless communication ranges with the ground stations preventing their control over large distances. Second, these low-cost aerial platforms have limited computation and energy resources preventing them from running heavy applications onboard. In this paper, we address this gap and we present Dronemap Planner (DP), a service-oriented cloud-based drone management system that controls, monitors and communicates with drones over the Internet. DP allows seamless communication with the drones over the Internet, which enables their control anywhere and anytime without restriction on distance. In addition, DP provides access to cloud computing resources for drones to offload heavy computations. It virtualizes the access to drones through Web services (SOAP and REST), schedules their missions, and promotes collaboration between drones. DP supports two communication protocols: (i.) the MAVLink protocol, which is a lightweight message marshaling protocol supported by commodities Ardupilot-based drones. (ii.) the ROSLink protocol, which is a communication protocol that we developed to integrate Robot Operating System (ROS)-enabled robots into the IoT. We present several applications and proof-of-concepts that were developed using DP. We demonstrate the effectiveness of DP through a performance evaluation study using a real drone for a real-time tracking application.
Anis Koubâa, Basit Qureshi, Mohamed-Foued Sriti, Azza Allouch, Yasir Javed, Maram Alajlan, Omar Cheikhrouhou, Mohamed Khalgui, Eduardo Tovar