Project Leader

Dr. Paolo Stegagno
Phone: +49 7071-601-218
Fax: +49 7071 601-616
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Recent Journal Publications

Grabe V, Bülthoff HH, Scaramuzza D and Robuffo Giordano P (July-2015) Nonlinear ego-motion estimation from optical flow for online control of a quadrotor UAV International Journal of Robotics Research 34(8) 1114-1135.
Ryll M, Bülthoff HH and Robuffo Giordano P (February-2015) A Novel Overactuated Quadrotor Unmanned Aerial Vehicle: Modeling, Control, and Experimental Validation IEEE Transactions on Control Systems Technology 23(2) 540-556.
Zelazo D, Franchi A, Bülthoff HH and Robuffo Giordano P (January-2015) Decentralized rigidity maintenance control with range measurements for multi-robot systems International Journal of Robotics Research 34(1) 105-128.
Franchi A, Oriolo G and Stegagno P (September-2013) Mutual Localization in Multi-Robot Systems using Anonymous Relative Measurements International Journal of Robotics Research 32(11) 1302-1322.
Lee D, Franchi A, Son HI, Ha CS, Bülthoff HH and Robuffo Giordano P (August-2013) Semiautonomous Haptic Teleoperation Control Architecture of Multiple Unmanned Aerial Vehicles IEEE/ASME Transactions on Mechatronics 18(4) 1334-1345.
Son HI, Franchi A, Chuang LL, Kim J, Bülthoff HH and Robuffo Giordano P (April-2013) Human-Centered Design and Evaluation of Haptic Cueing for Teleoperation of Multiple Mobile Robots IEEE Transactions on Cybernetics 43(2) 597-609.
Censi A, Franchi A, Marchionni L and Oriolo G (April-2013) Simultaneous Calibration of Odometry and Sensor Parameters for Mobile Robots IEEE Transaction on Robotics 29(2) 475-492.
Robuffo Giordano P, Franchi A, Secchi C and Bülthoff HH (March-2013) A Passivity-Based Decentralized Strategy for Generalized Connectivity Maintenance International Journal of Robotics Research 32(3) 299-323.
Franchi A, Secchi C, Son HI, Bülthoff HH and Robuffo Giordano P (October-2012) Bilateral Teleoperation of Groups of Mobile Robots with Time-Varying Topology IEEE Transaction on Robotics 28(5) 1019-1033.
Franchi A, Masone C, Grabe V, Ryll M, Bülthoff HH and Robuffo Giordano P (October-2012) Modeling and Control of UAV Bearing-Formations with Bilateral High-Level Steering International Journal of Robotics Research 31(12) 1504-1525.
Franchi A, Secchi C, Ryll M, Bülthoff HH and Robuffo Giordano P (September-2012) Shared Control: Balancing Autonomy and Human Assistance with a Group of Quadrotor UAVs IEEE Robotics & Automation Magazine 19(3) 57-68.

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Indoor localization of UAVs

In recent years, Unmanned Aerial Vehicles (UAVs) have attracted society in terms of economic and scientific scenarios, and are employed to a large extent in civilian and military applications. UAVs play a pivotal role, as they can perform tasks in highly hostile environments where access to humans is limited. UAVs that can autonomously operate in indoor environments are envisioned to be useful for a variety of applications including surveillance and search and rescue. For all these application an imperative need for UAV autonomy is the ability to self-localization in the environment. Indeed, precise localization is crucial in order to achieve high performance flight and to interact with the environment. Increasing innovation in the field of electronic communications has led to a current trend of utilizing sensing system such as Global positioning system (GPS), dead reckoning system, radio technologies or vision-based solutions for localization of UAVs.

Fusing data from different sensors helps to improve performance of the overall sensing system. For aerial navigation outdoors, fusion of GPS measurements with INS measurements by means of filtering techniques delivers the level of localization precision required by UAV missions. However GPS based localization remains impractical in indoor buildings. The signals received from the GPS based satellites cannot penetrate most buildings and materials. Although, one manages receive the GPS signal at indoors, the signal strength is not sufficient enough and remains a limiting factor for the performance of GPS. Hence GPS becomes totally ineffectual inside buildings. The alternative for localization in indoor environment is radio based localization systems. Radio based localization systems uses radio frequencies to determine a position on the space. The measurement is made back and forth by the radio beacons. The estimated position is calculated by several means such as measuring travel period (distance estimate), inferring direction (radio phases), interferometry, and velocity estimation (Doppler shift).

The challenge that we are addressing is to develop a 3D indoor localization system that can be an efficient and viable solution for UAV indoor localization. In our research group, we address the use of onboard vision systems, but also the exploitation of data collected from RFID sensor measurement an intenal INS (IMU) measurements.  We used the Vicon system to track the UAV navigation trajectories, thus providing an external localization reference for ground thrust.

More details will appear shortly.

Last updated: Friday, 13.02.2015