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 und Robuffo Giordano P (Juli-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 und Robuffo Giordano P (Februar-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 und Robuffo Giordano P (Januar-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 und 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 und 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 und 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 und 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 und Bülthoff HH (März-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 und Robuffo Giordano P (Oktober-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 und Robuffo Giordano P (Oktober-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 und 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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Shared Interactive Planning

Click to Enlarge.
Mobile robots are an important subject of research and investments because of their capability to perform efficiently and safely a large variety of tasks, even in dangerous environments. Possible applications of mobile robots  include exploration, surveillance, monitoring, search-and-rescue, good transportation, mobile-networking, etc.. In all these scenarios, the task needs to be translated by an algorithm into a trajectory to be tracked by the robot. There are several complex issues to be considered in this planning phase:
  • Time-varying tasks
  • Unstructured environment (obstacles, multiple locations of interest, ...)
  • Robot constraints (smooth enough trajectories, limited velocity, limited acceleration, ...)
  • Human interface (required for safety regulations)

This motivates our proposal of a shared interactive planning framework that systematically integrates  high-level/cognitive-enabled online planners algorithms and low-level/reactive trajectory modifiers. The main ideas developed are:

  • Systematic integration of different motion planning layers
  • Use of general trajectory parameterization
  • Real-time planning
  • Autonomous corrections (feasibility, obstacle avoidance, locations of interest)
  • Seamless inclusion of a human operator in the planning loop
Visit Carlo Masone's page for additional information. 


The proposed framework has a modular structure.

  1. A task scheduler provides offline  an initial nominal trajectory;
  2. a middle-level planner updates in real-time the initial plan and provides the desired trajectory;
  3. a low-level reactive layer corrects the desired trajectory to meet the following specifications:
    • ensuring feasibility of the motion given the robot constraints
    • the tracked trajectory remains close to the desired one
    • ensure obstacle avoidance
    • pass close to the points-of-interest near the reference trajectory

Our approach exploits the subdivision of trajectories in geometric path and timing law to simplify the planning layers. A set of geometric attributes is used to define the system state variables that control the shape of the path, thus providing an intuitive way for a human operator to interact with the planner. A decoupling mechanism is introduced to cancel the instantaneous effect of path variations on the motion references for the robot. Therefore, the instantaneous motion commands are affected only by the timing-law.

Validation and Additional Features

We have validated the framework with extensive human-in-the-loop simulations. We have also implemented several additional features

  • Interface for bilateral control
  • Velocity scaling for obstacle avoidance
  • Autonomous generation of alternative paths in cluttered environments
  • Automatic generation of points-of-interest for a coverage task

Essential Publications in this Topic

Franchi A, Masone C und Robuffo Giordano P (Oktober-2012) A Synergetic High-level/Reactive Planning Framework with Application to Human-Assisted Navigation, 2012 IEEE IROS Workshop on Robot Motion Planning: Online, Reactive, and in Real-time, 15-20.
pdfCiteID: FranchiMR2012
Masone C, Franchi A, Bülthoff HH und Robuffo Giordano R (Oktober-2012) Interactive Planning of Persistent Trajectories for Human-Assisted Navigation of Mobile Robots, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), IEEE, Piscataway, NJ, USA, 2641-2648.
pdfCiteID: MasoneFBR2012

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Last updated: Freitag, 13.02.2015