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An Implementation of Artificial Physics Using
AIBO Robots and the Pyro Programming Environment
Ankur Desai
December 7, 2006
Naval Research Laboratories Artificial Intelligence Center
4555 Overlook Ave., SWWashington, DC 20375
Mitchell A. Potter, Ph.D.
Principal Investigator
Evolutionary RoboticsCoevolutionary ModelsRepresentation IssuesContinuous and Embedded Learning
Adaptive Systems Team
Shared lab space
Variety of robotic equipment
No wireless communications
Upcoming anniversary demonstration
Rationale
Divide tasks between multiple robots
Based on natural behaviors
Unified platforms
Purpose
Determine whether AIBO is an effective platform for artificial physics
Create Python module to control the AIBO robots
Artificial Physics
Developed by Spears and Gordon in 1999
Each robot treated as a molecule
Gravitational forces simulated
Artificial Physics
Grid formation Resource protection
Sony AIBO
Python Robotics
Interpreted language
Platform-blind
High-level control
Testing Design
Straight line accuracy
Turning accuracy
Correct functioning of simulationNo testing necessary
Materials
SoftwareSWIGTekkotsuPyro
Seven AIBO robots
Procedures – Python module
Build C library object files
Create SWIG wrapper
Compile wrapper into dynamic library
Procedures – OdometrySetup
Place AIBO in empty roomConnect to host computer
Send commandWalk 10 metersTurn 360°
Measure actual motion
Straight Line Results
0
2
4
6
8
10
12
Accuracy of Straight Line Odometry
WalkCrawl
Trial
Dis
tan
ce (
m)
Accuracy of Straight Line Odometry Data
10 9.44 -5.6 9.86 -1.410 4.7 -53 10.92 9.210 6.04 -39.6 5.68 -43.210 10.46 4.6 9.14 -8.610 5.24 -47.6 7.46 -25.410 10.7 7 3.6 -6410 9.42 -5.8 10.68 6.810 4.8 -52 10.42 4.210 6.5 -35 7.22 -27.810 4.24 -57.6 9.9 -1
Expected (meters)
Measured Walking (m)
Error (%)
Measured Crawling (m)
Error (%)
Turning Results
0
50
100
150
200
250
300
350
400
450
Accuracy of Turning Odometry
WalkCrawl
Trial
An
gle
(°)
Accuracy of Turning Odometry Data
Error (%) Error (%)360 220 -38.89 320 -11.11360 340 -5.56 250 -30.56360 190 -47.22 230 -36.11360 390 8.33 170 -52.78360 360 0 400 11.11360 330 -8.33 260 -27.78360 340 -5.56 390 8.33360 380 5.56 220 -38.89360 230 -36.11 340 -5.56360 360 0 190 -47.22
Expected (degrees)
Measured Walking (°)
Measured Crawling (°)
Conclusion
Python module successful
AIBO is not a suitable platform
Alternate localization techniques
Use of different robotic models
Reflections
Overall positive experience
Delayed security clearance
Limited wireless access
Difficult commute
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.
Literature CitedBlank, 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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