In September 2019 we participated in our second aerial competition in Madrid, the “International Micro Air Vehicle Competition and Conference”, short IMAV. This competition is sponsored by Airbus, Parrot, the Spanish Postal Service, Correos, and many more companies and this year it was held at the polytechnical university of Madrid  

With our quadcopters for autonomous indoor navigation, we accomplished the 3rd place and the special award for the best package-handling. 12 international teams from four differenn participated in the competition. Our interdisciplinary team combined expertise in the fields of computer science, mechanical engineering,and electrical engineering. 

Our team sends special thanks to the Voss foundation and numerous moren sponsors in the drone industry for their aid and commitment. 

The goal was the development of autonomous drones for the use in an autonomous warehouse. Among other use cases cupboards were supposed to be taken inventory of and cargo was to be transported, while the drone navigates and operates completely autonomously.

Our team members designed the quadcopter from ground up and with our combined knowledge out of different engineering programmes (mechanical and electrical engineering). In preparation for the competition, we built two identical versions of the drone, so the tasks at the competition could be accomplished simultaneously. Furthermore, the two versions offered a failsafe in case of a software or hardware defect. The emphasis was put on the integration of all necessary components for the complete on-board calculation of the computer visual tasks. A further factor in the construction design was the feature of transporting and dropping cargo at a certain destination. As material we mainly used carbon-fibre plates, however the chassis is made from light wood. All parts were precisely crafted for this purpose and CNC milled. The camera mount, the chassis and the packaging were designed by our team and 3D printed.

The software stack of the “very hungry caterpillar”-drone is based on the robot operating system, short ROS, the most widely used platform for the development of robotics-applications. Furthermore, we used certain packages like MAVROS for the application and integration of the flight hardware into the navigation and the control system.  

Already in the prototype phase we realised that the calculating power of one computer is not sufficient at fulfilling all of the required tasks. Therefore, we made the decision to spread the task between a NVIDIA Jetson Nano and a Raspberry Pi 4. The Roscore was also handled by the Jetson Nano, our main device, while the Raspberry Pi communicated with the Jetson Nano via Ethernet. For the control of the flight hardware, we connected the Pixhawk flight controller via USB with the Jetson. For quick tests of our software stack and ROS knot we modelled the entire environment inside a Gazebo simulation. The T265 as well as the Picam were connected to the Jetson. In order to check the positional data of the T265 and to compensate for drift, we used Aruco markers as solid reference points in the world. For the marker recognition we used a camera on the belly of the drone. This way we were able to determine a global position within the environment very quickly and verify it. Two knots, which meld this data, were developed by our team (Mapper and Fusion).er Jetson Nano,  das Hauptgerät, hat auch den Roscore ausgeführt, während der Raspberry Pi über Ethernet mit dem Jetson Nano kommunizierte. Zur Steuerung der Flughardware wurde der pixhawk flight controller über USB mit dem Jetson verbunden. Zum schnellen Testen unseres Software-Stacks und unserer ROS-Knoten wurde die gesamte Umgebung für die Verwendung in einer Gazebo-Simulation modelliert. Die T265 sowie eine Picam wurden an den Jetson angeschlossen. Um die vom T265 gelieferten Positionsdaten zu überprüfen und Drift zu kompensieren, wurden Aruco-Marker als feste Referenzpunkte in der Welt verwendet. Für die Markererkennung wurde eine Kamera auf der Unterseite der Drohne verwendet. Auf diese Weise konnte eine globale Position in der Umwelt immer mit hoher Genauigkeit bestimmt und verifiziert werden. Zwei Knoten, um diese Daten zu verschmelzen, wurden vom Team entwickelt (Mapper und Fusion).

Empty Weight 1.200g 

MTOW: 2.500g 

Frame Size 330mm 

Time of construction and development: July 2019 – Aug. 2019 

Motors: 4x F80 Pro 2200kv 

Regulator: 4x HGLRC Forward FD50A 

Battery: 2p 14,8V 2600mAh LiPo (provided by MyLipoShop) 

Propeller: 7×3.8in 

max. thrust: 8.500g 

Special details 

• Intel Realsense T265 
• Nvidia Jetson Nano 
• Raspberry Pi 4 
• Holybro Pixhawk PX4 

• 2x Pi Cam 

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