This project aims to achieve coordination of multiple, fully autonomous, Unmanned Aerial Vehicles (UAVs), through the concept of robot formations. Through coordination, a system of multiple UAVs can mitigate the impact of the limited capabilities at the individual vehicle level, such as, limitations on carrying an object of a certain weight or complexity, or scanning the environment with limited Field of View (FOV) cameras.

Specifically, this project focus on achieving formations of multiple UAVs without using external localization sensors and computation for controlling the vehicles. This is acomplished by two steps. The first step consists of developing and deploying on the UAVs relative sensors that allow the UAVs to obtain the range and bearing with respect to each other. With those measurements, the UAVs can control their relative positions with respect to their team-mates and maintain formation. The second step consists on adapt the formation control algorithms in order to cope with the limitation of the relative sensors (e.g range and FOV).

To validate our approach, we run experiments an indoor facility equipped with a motion capture system, providing millimetric position accuracy. Our experiments are performed using small quadrotors that embed the developed relative sensors and a computation unit capable of processing the sensing data and the developed formation control methods.

Team and Collaborators

• Duarte Dias
• Steven Roelofsen
• William Christopher Evens
• Kağan Erunsal

In collaboration with:

• Rodrigo Ventura, Institute for Systems and Robotics, Instituto Superior Técnico, Portugal
• Pedro Lima, Institute for Systems and Robotics, Instituto Superior Técnico, Portugal

Research Period and Sponsors

This project started in September 2013 and is still ongoing.

This work has been partially supported by ISR/LARSyS Strategic Funds from FCT [UID/EEA/5009/2013] and FCT SFRH/BD/51928/2012.

Related Student Projects and Internships

DISAL-SP123: Valentin Kindschi, Adapting a quadrotor for odor source localization in a realistic environment
DISAL-IP29: Liam Hodgson, Coherent decentralized quadrotor formations in presence of sensor limitations
BURDICK-DISAL-MP23: Marc Schönenberger, Embedded avionics and attitude estimation for a mobile spherical autonomous robot
DISAL-SP68: Remi Siegfried, Dealing with failures in real quadrotor formations
DISAL-SP60: Florent Chèvre, Managing multiple quadrotors in formation control experiments

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