Abstract
This paper is focused on the sensor and information fusion techniques used by a robotic soccer team. Due to the fact that the sensor information is affected by noise, and taking into account the multi-agent environment, these techniques can significantly improve the accuracy of the robot world model. One of the most important elements of the world model is the robot self-localisation. Here, the team localisation algorithm is presented focusing on the integration of visual and compass information. To improve the ball position and velocity reliability, two different techniques have been developed. A study of the visual sensor noise is presented and, according to this analysis, the resulting noise variation depending on the distance is used to define a Kalman filter for ball position. Moreover, linear regression is used for velocity estimation purposes, both for the ball and the robot. This implementation of linear regression has an adaptive buffer size so that, on hard deviations from the path (detected using the Kalman filter), the regression converges more quickly. A team cooperation method based on sharing of the ball position is presented. Besides the ball, obstacle detection and identification is also an important challenge for cooperation purposes. Detecting the obstacles is ceasing to be enough and identifying which obstacles are team mates and opponents is becoming a need. An approach for this identification is presented, considering the visual information, the known characteristics of the team robots and shared localisation among team members. The same idea of distance dependent noise, studied before, is used to improve this identification. Some of the described work, already implemented before RoboCup2008, improved the team performance, allowing it to achieve the 1st place in the Portuguese robotics open Robótica2008 and in the RoboCup2008 world championship.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E.: RoboCup: The Robot World Cup Initiative. In: Proceedings of the first international conference on Autonomous agents, pp. 340–347 (1997)
MSL Technical Committee 1997-2008: Middle Size Robot League Rules and Regulations for 2008 (2007)
Durrant-Whyte, H., Henderson, T.: Multisensor Data Fusion. In: Springer Handbook of Robotics. Springer, Heidelberg (2008)
Lauer, M., Lange, S., Riedmiller, M.: Calculating the perfect match: an efficient and accurate approach for robot self-localization. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 142–153. Springer, Heidelberg (2006)
Neves, A., Martins, D., Pinho, A.: A hybrid vision system for soccer robots using radial search lines. In: Proc. of the 8th Conference on Autonomous Robot Systems and Competitions, Portuguese Robotics Open - ROBOTICA 2008, Aveiro, Portugal, pp. 51–55 (2008)
Lauer, M., Lange, S., Riedmiller, M.: Modeling Moving Objects in a Dynamically Changing Robot Application. In: Furbach, U. (ed.) KI 2005. LNCS (LNAI), vol. 3698, pp. 291–303. Springer, Heidelberg (2005)
Xu, Y., Jiang, C., Tan, Y.: SEU-3D 2006 Soccer Simulation Team Description. In: CD Proc. of RoboCup Symposium 2006 (2006)
Marcelino, P., Nunes, P., Lima, P., Ribeiro, M.I.: Improving object localization through sensor fusion applied to soccer robots. In: Proc. Scientific Meeting of the Portuguese Robotics Open - Robótica 2003 (2003)
Ferrein, A., Hermanns, L., Lakemeyer, G.: Comparing Sensor Fusion Techniques for Ball Position Estimation. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 154–165. Springer, Heidelberg (2006)
Bishop, G., Welch, G.: An Introduction to the Kalman Filter. In: Proc of SIGGRAPH, Course 8. Number NC 27599-3175, Chapel Hill, NC, USA (2001)
Motulsky, H., Christopoulos, A.: Fitting models to biological data using linear and nonlinear regression. GraphPad Software Inc. (2003)
Neves, A., Corrente, G., Pinho, A.: An omnidirectional vision system for soccer robots. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 499–507. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Silva, J., Lau, N., Rodrigues, J., Azevedo, J.L., Neves, A.J.R. (2010). Sensor and Information Fusion Applied to a Robotic Soccer Team. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds) RoboCup 2009: Robot Soccer World Cup XIII. RoboCup 2009. Lecture Notes in Computer Science(), vol 5949. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11876-0_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-11876-0_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11875-3
Online ISBN: 978-3-642-11876-0
eBook Packages: Computer ScienceComputer Science (R0)