Seminar: Mobile Robotics

Aufgrund der geringen Nachfrage findet das Proseminar nicht statt.
Lecturers Yann Berquin, Adrian Zwiener,
Consultation-Hour entfällt
Time entfällt
Umfang 2 SWS, 4 LP
Beginn entfällt
First Meeting Thursday 15. October 2015, 15 Uhr
Room Sand 1, Raum A302



All students present their work in a 45 min presentation plus 10 min discussion. Two weeks after the presentation an essay on the topic has to be turned in (12-15 pages).


  • Path Planning for Autonomous Vehicles
    Path Planning is neccessary if a vehichle drives autonomous from a start pose to an goal pose. Issues are search algorithms like A* or D* and rapidly exploring random trees and obstacle avoidance. References: [1-5]
  • Control: Path Following
    Having found a save path from the robots starting point to the goal, we have to make sure that the robot follows this path. However, inaccuracy in steering and the properties of the environment leads to discrepancies to the planed path. This topics deals with minimizing this discrepancies and control in general. References: [1,5-9]
  • Control of Robotic Arms
    The control of robotic arms is complex due to the many degrees of freedoms. Besides, in manipulation gravity plays a more important role than for mobile robots. Consequently, dynamic models are necessary. On top of that, torque and force control provide nice applications. Ideally students should have knowledge of Physics in particular Mechanics. References[1,10-12]
  • Motion Planning for Robotic Arms
    The use of a robotic arms requires methods determining the angle of each joint motor for a given time to reach an goal position without any collisions. Consequently, motion planning for robotic arms involves much more degrees of freedom than the motion planning for the robots pose. Though the workspace is much smaller. Motion Planning for arms gets even more complex if constraints are added to an trajectory e.g. the movement of a cup of coffee without spilling. Hence different techniques are needed, which are researched here. References: [1,13-15]
  • Detection of Grasping Points
    Handling of tools and the movement of objects requires the robot to grasp the object securely to prevent slippage. But where an object has to be grasped depends strongly on the geometry and surface condition of the object. The scope of this topic is how good grasping points can be found using computer vision and machine learning. References: [16,17]
  • Occupancy Grid Mapping
    We propose to study the problem of mapping using occupancy grids along with remote sensing (mainly laser ranging and sonar sensors). This topic can be a good introduction to data assimilation and Simultaneous Localization and Mapping (SLAM). Ideally students should have knowledge in probability and distributions. References: [18,19,28]
  • Sensor Fusion
    This topic focuses on the joint use of data from multiple sensors for navigation and mapping in mobile robotic applications. Ideally students should have interest and knowledge in probability and data assimilation. References: [20,21,22,23]
  • Multi Agent Systems
    This topic focuses on the use of multiple robots for mobile robotic applications. This topic allows to have an overview of general mobile robotics issues. This topic is best suited for first year university students. Students interested in game theory might also find this topic valuable. References: [22,23,24]
  • Advances in Robotic Exploration of Extreme Environments
    We propose here a quite general topic for students interested in space and ocean exploration. The topic is probably best suited for first year university students and can be a good introduction to mobile robotic applications. It offers also insight of most areas of research and issues that concern mobile robotics. References: [25,26,32]
  • Kalman Filtering and Probability
    This topic requires strong interest in mathematics. The goal of this topic is to study the relation between Kalman filter and probability distributions and to have an overview of Kalman filtering techniques more generally. Ideally students should have knowledge in linear algebra and probability. References: [23,28,29]
  • Monte Carlo Algorithms for Localization and Mapping
    This topic requires interest in mathematics and probability. It may be a useful complement or introduction to data assimilation. Some knowledge in probability would be helpful. References: [28,30,31]


[1]Bruno Siciliano and Oussama Khatib. 2007. Springer Handbook of Robotics. Springer-Verlag New York, Inc., Secaucus, NJ, USA.
[2]Dolgov, D., Thrun, S., Montemerlo, M., & Diebel, J. (2010). Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments. The International Journal of Robotics Research, 29(5), 485-501. doi:10.1177/0278364909359210.
[3]Stentz, a. (1994). Optimal and efficient path planning for partially-known environments. Proceedings of the 1994 IEEE International Conference on Robotics and Automation, (May). doi:10.1109/ROBOT.1994.351061
[4]LaValle, S. (1998). Rapidly-Exploring Random Trees: A New Tool for Path Planning.
[5]Kathib, O. (1986). Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research, 90-98.
[6]A. Pedro Aguiar , Dragan B. Dacic , João P. Hespanha, P. K., Aguiar, P., Da Ucic, D., Hespanha, J., & Kokotivic, P. (2004). Path-Following or Reference-Tracking? An answer relaxing the limits to performance. Proceedings of IAV2004 5th IFACEURON Symposium on Intelligent Autonomous Vehicles, 158(July), 1-6. doi:
[7]De Luca, A., Oriolo, G., & Samson, C. (1998). Feedback control of a nonholonomic car-like robot. Robot Motion Planning and Control, 171-253. doi:10.1007/BFb0036073
[8]Xiang Li, Dribbling Control of an Omnidirectional Soccer Robot, Dissertation, Tuebingen 2009
[9]A. Mojaev, A.Zell: Tracking Control and Adaptive Local Navigation for Nonholonomic Mobile Robots, In Proceedings of the IAS-8 conference, 2004
[10]Sanchez-Sanchez, P., & Arteaga-Perez, M. A. (2012). Simplied methodology for obtaining the dynamic model of robot manipulators. International Journal of Advanced Robotic Systems, 9. doi:10.5772/51305
[11] Spong, M. W. (1987). Modeling and Control of Elastic Joint Robots. Journal of Dynamic Systems, Measurement, and Control, 109(DECEMBER 1987), 310. doi:10.1115/1.3143860.
[12] Nolting, W., Grundkurs Theoretische Physik 1, 2004, Springer
[13]Bruce R. Donald, A search algorithm for motion planning with six degrees of freedom, Artificial Intelligence, Volume 31, Issue 3, March 1987, Pages 295-353.
[14]Zucker, M., Ratliff, N., Dragan, a. D., Pivtoraiko, M., Klingensmith, M., Dellin, C. M., … Srinivasa, S. S. (2013). CHOMP: Covariant Hamiltonian optimization for motion planning. The International Journal of Robotics Research, 32(9-10), 1164-1193. doi:10.1177/0278364913488805
[15]Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., & Schaal, S. (2011). STOMP: Stochastic trajectory optimization for motion planning. Proceedings - IEEE International Conference on Robotics and Automation, 4569-4574. doi:10.1109/ICRA.2011.5980280
[16]Fischinger, David, Astrid Weiss, and Markus Vincze. Learning grasps with topographic features. The International Journal of Robotics Research (2015): 0278364915577105.
[17]Jiang, Y., Moseson, S., & Saxena, A. (2011). Efficient grasping from RGBD images: Learning using a new rectangle representation. Proceedings - IEEE International Conference on Robotics and Automation, 3304-3311. doi:10.1109/ICRA.2011.5980145
[18]Elfes, A. (1989). Using occupancy grids for mobile robot perception and navigation. Computer, vol. 22, pp. 46-57.
[19]Thrun, S. (2003). Learning Occupancy Grid Maps with Forward Sensor Models. Autonomous Robots, vol. 15, pp. 111-127.
[20]Murphy, R. R. (1998). Dempster-Shafer theory for sensor fusion in autonomous mobile robots. IEEE Transactions on Robotics and Automation, vol. 14, pp. 197-206.
[21]Hackett, J. K. and M. Shah (1990). Multi-sensor fusion: a perspective. IEEE International Conference on Robotics and Automation, pp. 1324-1330.
[22]Burgard, W., M. Moors, D. Fox, R. Simmons and S. Thrun (2000). Collaborative Multi-Robot Exploration. Intl. Conf. on Robotics and Automation.
[23]Howard, A. (2006). Multi-robot Simultaneous Localization and Mapping using Particle Filters. International Journal of Robotics Research, vol. 25, pp. 1243-1256.
[24]Parker, L. E. (2008). Distributed Intelligence: Overview of the Field and its Application in Multi-Robot Systems. Journal of Physical Agents.
[25]Cully, A., J. Clune, D. Tarapore and J. B. Mouret (2015). Robots that can adapt like animals. Nature, vol. 521, pp. 503-507.
[26]SunSpiral, V., G. Gorospe, J. Bruce, A. Iscen, G. Korbel, S. Milam, A. Agogino and D. Atkinson (2013). Tensegrity based probes for planetary exploration: Entry, descent and landing (EDL) and surface mobility analysis. International Journal of Planetary Probes.
[27]Yuh, J. (2000). Design and control of autonomous underwater robots: A survey. Autonomous Robots, vol. 8, pp. 7-24.
[28]Tarantola, A. (2005). Inverse problem theory and methods for model parameter estimation. SIAM.
[29]Tarantola, A., L. Klimes, J. M. Pozo and B. Coll (2009). Gravimetry, relativity, and the global navigation satellite systems. arXiv preprint arXiv:0905.3798.
[30]Thrun, S., D. Fox, W. Burgard and F. Dellaert (2001). Robust Monte Carlo localization for mobile robots. Artificial Intelligence, vol. 128, pp. 99-141.
[31]Kümmerle, R., R. Triebel, P. Pfaff and W. Burgard (2008). Monte Carlo localization in outdoor terrains using multilevel surface maps. Journal of Field Robotics, vol. 25, pp. 346-359.
[32]Agogino, A., V. SunSpiral and D. Atkinson (2013). Super Ball Bot-Structures for Planetary Landing and Exploration. NASA report.

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