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An Implementation of Artificial Physics Using AIBO Robots and the Pyro Programming Environment Ankur Desai December 7, 2006
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Naval Research Laboratories Artificial Intelligence Center 4555 Overlook Ave., SW Washington, DC 20375
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Mitchell A. Potter, Ph.D. Principal Investigator Evolutionary Robotics Coevolutionary Models Representation Issues Continuous and Embedded Learning
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Adaptive Systems Team Shared lab space Variety of robotic equipment No wireless communications Upcoming anniversary demonstration
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Rationale Divide tasks between multiple robots Based on natural behaviors Unified platforms
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Purpose Determine whether AIBO is an effective platform for artificial physics Create Python module to control the AIBO robots
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Artificial Physics Developed by Spears and Gordon in 1999 Each robot treated as a molecule Gravitational forces simulated
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Artificial Physics Grid formationResource protection
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Sony AIBO
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Python Robotics Interpreted language Platform-blind High-level control
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Testing Design Straight line accuracy Turning accuracy Correct functioning of simulation No testing necessary
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Materials Software SWIG Tekkotsu Pyro Seven AIBO robots
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Procedures – Python module Build C library object files Create SWIG wrapper Compile wrapper into dynamic library
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Procedures – Odometry Setup Place AIBO in empty room Connect to host computer Send command Walk 10 meters Turn 360° Measure actual motion
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Straight Line Results
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Turning Results
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Conclusion Python module successful AIBO is not a suitable platform Alternate localization techniques Use of different robotic models
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Reflections Overall positive experience Delayed security clearance Limited wireless access Difficult commute
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Acknowledgments I would like to thank the Adaptive Systems team at Naval Research Laboratories Artificial Intelligence Center, especially Mitchell Potter and R. Paul Wiegand, for their guidance and support throughout this project.
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Literature Cited Blank, D., Meeden, L., & Kumar, D. (2003). Python robotics: An environment for exploring robotics beyond LEGOs. SIGSCE ’03, 35, 317-3121. Ikemoto, Y., Hasegawa, Y., Fukuda, T., & Matsuda, K. (2005). Gradual spatial pattern formation of homogeneous robot group. Information Sciences, 171, 431-445. Lee, M. (2003). Evolution of behaviors in autonomous robot using artificial neural network and genetic algorithm. Information Sciences, 155, 43-60. Oliveira, E., Fischer, K., & Stepankova, O. (1999). Multi-agent systems: Which research for which applications. Robotics and Autonomous Systems, 27, 91-106. Röfer, T., & Jüngel, M. (2003). Fast and robust edge-based localization in the Sony four-legged robot league. In Polani, D., Browning, B., Bonarini, A., & Yoshida, K. (Eds.), RoboCup 2003: Robot soccer world cup VII (pp. 262-273). Berlin: Springer. Spears, W. M., & Gordon, D. F. (1999). Using artificial physics to control agents. 1999 International Conference on Information Intelligence and Systems, 1999, 281- 288. Tira-Thompson, E. J., Halelamien, N. S., Wales, J. J., & Touretzky, D. S. (2004). Tekkotsu: Cognitive robotics on the Sony AIBO. Proceedings of the Sixth International Conference on Cognitive Modeling, 6, 390-391.
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