Attempto Tuebingen

Organisation: Eberhard Karls Universität Tübingen

Website: http://www.cogsys.cs.uni-tuebingen.de/

Address: Sand 1, 72076 Tübingen, Germany

Contact: Prof. Dr. Andreas Zell (Tel: +49 7071 29 76455, Andreas.zell(at)uni-tuebingen.de)

 

Other Staff:

  • Sebastian Scherer (Universität Tübingen)
  • Radouane Ait-Jellal (Universität Tübingen)
  • Yuyi Liu (Universität Tübingen)
  • Shaowu Yang (Universität Tübingen)
  • Konstantin Schauwecker (Universität Tübingen)
  • Dr. Paolo Stegagno (MPI for Biolog. Cybernetics)
  • Marcin Odelga (MPI for Biolog. Cybernetics)
  • Carlo Masone (MPI for Biolog. Cybernetics)
  • Sujit Rajappa (MPI for Biolog. Cybernetics)

 

Team Profile:

The team Attempto Tuebingen is a joint team comprising approx. 10 researchers from the Chair of Cognitive Systems (Prof. Zell), CS Dept., University of Tuebingen, and the MPI for Biological Cybernetics, Dept. Human Perception, Cognition and Action (Prof. Buelthoff). The MPI group on autonomous robotics and human machine system is led by Dr. Paolo Stegagno.

Both groups have a long lasting track record for working with unmanned aerial vehicles (UAVs), mainly with quadrocopters. Each group has more than 10 quadrocopters (Microkopter, ETHZ Pixhawk, and Asctec Hummingbird) able to fly autonomously, and has published on aerial vehicles, robot vision, visual SLAM or control of UAVs. Both groups are well known in the mobile robotics community and have published intensively in the last 10 years on UAVs at IEEE ICRA and IROS, IEEE RAS, IJRR, IAS, and other top robotic conferences and journals.

The MPI group is focusing on the study of novel control, estimation and planning techniques for UAVs that are able to sense the environment, reason about it and take action to perform some tasks. The Uni Tübingen group is focusing on robot vision, sensor integration, stereo vision, visual SLAM, RGB-D sensors. In this way the two groups complement each other nicely. Both groups have the goal to do all the sensing and processing onboard, in order to have full autonomy of their UAVs, capable to fly outside of tracking labs.

Prof. Dr. Andreas Zell will be the team leader and control the task distribution and cooperation in the whole team, consisting to the two groups from the Univ. of Tübingen and the MPI for biological cybernetics.

Sebastian Scherer will contribute as supervisor and active developer for both task 1 (Localization) and 2 (Mapping). He will apply methods he developed for visual SLAM integrating depth measurements [11], which has been used for enabling MAVs equipped with RGB-D or stereo cameras to fly autonomously [12],[13],[14]. He will also evaluate different algorithms for occupancy grid mapping in task 2.

Yuyi Liu will contribute both to task 3 and 4, by working with MPI on model based controller. For task 3, he will work on the development of a hovering controller based on back-stepping control and an attitude controller based on output regulation. For task 4 he will contribute to trajectory planners.

Radouane Ait-Jellal will contribute to Task 1 and Task 2 (mapping). He will develop an efficient algorithm for dense stereo matching which preserves disparity discontinuities and detects and handles (half) occlusions. This stereo algorithm can be integrated in the localization algorithm and in the reconstruction algorithm. Speeding up Konstantin's Octomap based map builder while keeping it "reasonably" accurate.

Konstantin Schauwecker will contribute to Task 1 and 2 by providing implementations of his methods for stereo vision based autonomous navigation [12][14] and stereo vision based occupancy mapping [15]. In later contest stages he will work on real-time dense stereo matching approaches.

Dr. Paolo Stegagno will contribute as supervisor and active developer for both task 3 and 4. Paolo will design and implement the required estimators, help in developing the controllers and planners, and supervise the interconnection of the whole system. Paolo may also contribute to task 1 or 2 e.g.: for state estimation and advanced filtering methods for the individuation of non-static obstacles (through PHD filter).

Marcin Odelga will contribute both to task 3 and 4. For task 3, Marcin will work on the development of a robust controller based on [4] or on sliding mode controller for hovering. He will also interface the Telekyb software with the provided simulator, to benefit from many already implemented methods (controllers, state estimators, planners ...). In task 4 he will help in the development of planning methods. [9], [10].

Carlo Masone will contribute to both task 3 and 4. For task 3, he will contribute in the development of a robust controller. For task 4, Carlo will bring the know-how on obstacle avoidance and real time replanning on UAVs, which are also one of the topics of his Ph.D. thesis. We plan to adapt and apply the methods developed in [1] and [2] for obstacle avoidance and online replanning.

Sujit Rajappa will contribute primarily to task 3, designing and developing an estimator for external disturbances and applying it in an adaptive control scheme for the stabilization of the UAV, both in hovering and during navigation.

 

YouTube channels:

http://www.youtube.com/user/ZellTuebingen

http://www.youtube.com/user/MPIRobotics

 

List of recent relevant publications:

  1. C. Masone, A. Franchi, H. H. Bülthoff, and P. Robuffo Giordano. Interactive planning of persistent trajectories for human-assisted navigation of mobile robots. In IROS 2012, pages 2641–2648, Vilamoura, Portugal
  2. C. Masone, P. Robuffo Giordano, H. H. Bülthoff, and A. Franchi. Semi-autonomous trajectory generation for mobile robots with integral haptic shared control. ICRA 2014, Hongkong
  3. A. Franchi, C. Masone, V. Grabe, M. Ryll, H. H. Bülthoff, and P. Robuffo Giordano. Modeling and control of UAV bearing-formations with bilateral high-level steering. The Int. Journal of Robotics Research, Special Issue on 3D Exploration, Mapping, and Surveillance, 31(12):1504–1525, 2012
  4. G. Antonelli, E. Cataldi, P. Robuffo Giordano, S. Chiaverini and A. Franchi. Experimental validation of a new adaptive control scheme for quadrotors MAVs. In IROS-2013, pages 2439-2444, Tokyo, Japan, Nov. 2013
  5. P. Stegagno, M. Basile, H. H. Bülthoff and A. Franchi. A Semi-autonomous UAV Platform for Indoor Remote Operation with Visual and Haptic Feedback. In ICRA 2014 - accepted
  6. P. Stegagno, M. Basile, H. H. Bülthoff and A. Franchi. Vision-based Autonomous Control of a Quadrotor UAV using an Onboard RGB-D Camera and its Application to Haptic Teleoperation. In RED-UAS 2013, pages 87-92, Compiegne, France, Nov. 2013
  7. P. Stegagno, M. Cognetti, L. Rosa, P. Peliti and G. Oriolo. Relative localization and identification in a heterogeneous multi-robot system. In ICRA 2013, pages 1857-1864, Karlsruhe, Germany, May 2013
  8. A. Franchi, G. Oriolo and P. Stegagno Mutual Localization in Multi-Robot Systems using Anonymous Relative Measurements. In Int. Journal of Robotics Research 32(11) 1302-1322, Sept. 2013
  9. M.Odelga, A. Chriette, F. Plestan. Control of 3 DOF helicopter: a novel autopilot scheme based on adaptive sliding mode control. In 2012 American Control Conference, Montreal, Canada, 2012
  10. A. Chriette, F. Plestan, M. Odelga. Nonlinear modeling and control of a 3DOF helicopter. In 2012 ASME Conference on Engineering Systems Design and Analysis, Nantes, France, 2012
  11. S. A. Scherer, D.Dube, and A. Zell. Using Depth in Visual Simultaneous Localisation and Mapping. In IEEE International Conference on Robotics and Automation, St. Paul, Minnesota, USA, May 2012
  12. K. Schauwecker, N. R. Ke, S. A. Scherer, and A. Zell. Markerless Visual Control of a Quad-Rotor Micro Aerial Vehicle by Means of On-Board Stereo Processing. In 22nd Conference on Autonomous Mobile Systems (AMS), pages 11-20, Stuttgart, Germany, September 2012. Springer
  13. S. A. Scherer and A. Zell. Efficient Onboard RGBD-SLAM for Fully Autonomous MAVs. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), Tokyo Big Sight, Japan, November 2013
  14. K. Schauwecker and A. Zell. On-Board Dual-Stereo-Vision for the Navigation of an Autonomous MAV. Journal of Intelligent & Robotic Systems, 74(1-2):1-16, January 2014
  15. K. Schauwecker and A. Zell. Robust and Efficient Volumetric Occupancy Mapping with an Application to Stereo Vision. In IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, May 2014
  16. S. Yang, S. A. Scherer, and A. Zell. An onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle. Journal of Intelligent & Robotic Systems, 69(1-4):499-515, January 2013
  17. S. Yang, S. A. Scherer, K. Schauwecker, and A. Zell. Autonomous Landing of MAVs on Arbitrarily Textured Landing Sites using Onboard Monocular Vision. Journal of Intelligent & Robotic Systems, 74(1-2):27-43, 2014

 

 

 

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