Cooperative World Modeling in Dynamic Multi-Robot Environments

• Autorzy: Göhring D , Burkhard H-D.
• Języki publikacji: EN

Abstrakty

EN

In this paper we describe how a group of agents can commonly estimate the position of objects. Furthermore we will show how these modeled object positions can be used for an improved self localization. Modeling of moving objects is commonly done by a single agent and in a robo-centric coordinate frame because this information is sufficient for most low level robot control and it is independent of the quality of the current robot localization. Especially when many robots cooperate with each other in a partially observable environment they have to share and to communicate information. For multiple robots to cooperate and share information, though, they need to agree on a global, allocentric frame of reference. But when transforming the egocentric object model into a global one, it inherits the localization error of the robot in addition to the error associated with the egocentric model. We propose using the relation of objects detected in camera images to other objects in the same camera image as a basis for estimating the position of the object in a global coordinate system. The spacial relation of objects with respect to stationary objects (e.g., landmarks) offers several advantages: The information is independent of robot localization and odometry and it can easily be communicated. We present experimental evidence that shows how two robots are able to infer the position of an object within a global frame of reference, even though they are not localized themselves. We will also show, how to use this object information for self localization. A third aspect of this work will cope with the communication delay, therefore we will show how the Hidden Markov Model can be extended for distributed object tracking.

Słowa kluczowe

EN

Markov localization; multi-agent systems

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2007
• Tom: Vol. 75, nr 1-4
• Strony: 281--294
• Opis fizyczny: bibliogr. 16 poz., rys.

Twórcy

autor

Göhring D

autor

Burkhard H-D.

• LFG Künstliche Inteligenz, Institut für Informatik, HU-Berlin, Unter den Linden 6, D-10099 Berlin,

Bibliografia

• [1] Wikipedia-bayesian network, September 2006.
• [2] R. Arkin. Behavior-Based Robotics. MIT Press, Cambridge, MA, USA, 1998.
• [3] J. Burlet, O. Aycard, and T. Fraichard. Robust navigation using markov models. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005.
• [4] M. Dietl, J. Gutmann, and B. Nebel. Cooperative sensing in dynamic environments. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'01), Maui, Hawaii, 2001.
• [5] D. Fox, W. Burgard, F. Dellaert, and S. Thrun. Monte carlo localization: Efficient position estimation for mobile robots. In Proceedings of the Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence (AAAI), pages 343-349. The AAAI Press/The MIT Press, 1999.
• [6] F. V. Jensen. Bayesian Networks and Decision Graphs. Springer, 2001.
• [7] K. Kaplan, B. Celik, T. Mericli, C. Mericli, and L. Akin. Practical extensions to vision-based monte carlo localization methods for robot soccer domain. In I. Noda, A. Jacoff, A. Bredenfeld, and Y. Takahashi, editors, 9th International Workshop on RoboCup 2005 (Robot World Cup Soccer Games and Conference), Lecture Notes in Artificial Intelligence. Springer, 2006. To appear.
• [8] H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, and E. Osawa. RoboCup: The robot world cup initiative. In W. L. Johnson and B. Hayes-Roth, editors, Proceedings of the First International Conference on Autonomous Agents (Agents'97), pages 340-347, New York, 5-8, 1997. ACM Press.
• [9] C. Kwok and D. Fox. Map-based multiple model tracking of a moving object. In D. Nardi, M. Riedmiller, C. Sammut, and J. Santos-Victor, editors, 8th International Workshop on RoboCup 2004 (Robot World Cup Soccer Games and Conferences), volume 3276 of Lecture Notes in Artificial Intelligence, pages 18-33. Springer, 2005.
• [10] S. Lenser, J. Bruce, and M. Veloso. CMPack: A complete software system for autonomous legged soccer robots. In AGENTS '01: Proceedings of the fifth international conference on Autonomous agents, pages 204-211. ACM Press, 2001.
• [11] S. Lenser and M. M. Veloso. Sensor resetting localization for poorly modelled mobile robots. In Proceedings of the 2000 IEEE International Conference on Robotics and Automation (ICRA 2000), pages 1225-1232. IEEE, 2000.
• [12] M. Montemerlo and S. Thrun. Simultaneous localization and mapping with unknown data association using FastSLAM. In Proceedings of the 2003 IEEE International Conference on Robotics and Automation (ICRA), pages 1985-1991. IEEE, 2003.
• [13] J. Pearl. Fusion, propagation, and structuring in belief networks. In Artificial Intelligence, volume 29, pages 241-288, 1986.
• [14] T. R¨ofer and M. Jüngel. Vision-based fast and reactive monte-carlo localization. In D. Polani, A. Bonarini, B. Browning, and K. Yoshida, editors, Proceedings of the 2003 IEEE International Conference on Robotics and Automation (ICRA), pages 856-861. IEEE, 2003.
• [15] S. Thrun, D. Fox, and W. Burgard. Monte carlo localization with mixture proposal distribution. In Proceedings of the National Conference on Artificial Intelligence (AAAI), pages 859-865, 2000.
• [16] S. Thrun, D. Fox, W. Burgard, and F. Dellaert. Robust monte carlo localization for mobile robots. Technical Report CMU-CS-00-125, School of Comuputer Science, Carnegie Mellon University, April 2000.