Action control of soccer robots based on simulated human intelligence

A multi-modal action control approach is proposed for an autonomous soccer robot when the bottom hardware is unchangeable. Different from existing methods, the proposed control approach defines actions with the principle of “perception-planning-action” inspired by human intelligence. Character extraction is used to divide the perception input into different modes. Different control modes are built by combining different control methods for the linear velocity and angular velocity. Based on production rules, the motion control is realized by connecting different perceptions to the corresponding control mode. Simulation and real experiments are conducted with the middle-sized robot Frontier-I, and the proposed method is compared with a proportional-integral-derivative (PID) control method to display its feasibility and performance. The results show that the multi-modal action control method can make robots react rapidly in a dynamic environment.

[1]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[2]  Kin Huat Low.  Locomotion and depth control of robotic fish with modular undulating fins , 2006, Int. J. Autom. Comput..

[3]  Wook Hyun Kwon,et al.  Rapid Control Prototyping for Robot Soccer System using SIMTool , 2006, 2006 SICE-ICASE International Joint Conference.

[4]  Hadas Kress-Gazit,et al.  Transforming high level tasks to low level controllers , 2008 .

[5]  Fang Yu,et al.  Target Localization for Autonomous Soccer Robot Based on Vision Perception , 2008, ISICA.

[6]  Maryline Chetto,et al.  A concept of dynamically reconfigurable real-time vision system for autonomous mobile robotics , 2008, Int. J. Autom. Comput..

[7]  Atsushi Imiya,et al.  Corridor Navigation and Obstacle Avoidance using Visual Potential for Mobile Robot , 2007, Fourth Canadian Conference on Computer and Robot Vision (CRV '07).

[8]  Dusan M. Stipanovic,et al.  Formation Control and Collision Avoidance for Multi-agent Non-holonomic Systems: Theory and Experiments , 2008, Int. J. Robotics Res..

[9]  Bin Li,et al.  Analysis of creeping locomotion of a snake robot on a slope , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[10]  David E. Goldberg,et al.  The multi-label OCS with a genetic algorithm for rule discovery: implementation and first results , 2009, GECCO '09.

[11]  A. Guillot,et al.  Adaptive motivation in a biomimetic action selection mechanism , 2008 .

[12]  Kazuo Ishii,et al.  A switching controller system for a wheeled mobile robot , 2007 .

[13]  Kazuo Yoshida,et al.  Action control method for mobile robot considering uncertainty of information , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Fares Boudjema,et al.  Car-Like Mobile Robot Oriented Positioning by Fuzzy Controllers , 2008 .

[15]  Robert J. Wood,et al.  Towards a 3g crawling robot through the integration of microrobot technologies , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[16]  Manuela M. Veloso,et al.  Cooperative 3-Robot Passing and Shooting in the RoboCup Small Size League , 2006, RoboCup.

[17]  Gal A. Kaminka,et al.  Flexible Teamwork in Behavior-Based Robots , 2005, AAAI.

[18]  Peter Kopacek,et al.  Neural Networks for the Control of Soccer Robots , 2004, Fuzzy Days.

[19]  Dirk Thomas,et al.  New Autonomous, Four-Legged and Humanoid Robots for Research and Education , 2008 .

[20]  Javier Ruiz-del-Solar,et al.  Probabilistic Decision Making in Robot Soccer , 2007, RoboCup.

[21]  Bernd Reusch.  Computational Intelligence, Theory and Applications , 1997 .

[22]  Ying Zhang,et al.  A rough set GA-based hybrid method for robot path planning , 2006, Int. J. Autom. Comput..