TWO METHODOLOGIES TOWARD ARTIFICIAL TACTILE AFFORDANCE SYSTEM IN ROBOTICS

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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VOLUME 3 , ISSUE 3 (September 2010) > List of articles

TWO METHODOLOGIES TOWARD ARTIFICIAL TACTILE AFFORDANCE SYSTEM IN ROBOTICS

M. Ohka * / N. Hoshikawa / J. Wada / H. B. Yussof

Keywords : Affordance, Tactile sensor, Three-axis, Collective robot, Evolution, Behavior, Genetic algorithm

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 3, Issue 3, Pages 466-487, DOI: https://doi.org/10.21307/ijssis-2017-403

License : (CC BY-NC-ND 4.0)

Published Online: 13-December-2017

ARTICLE

ABSTRACT

If the theory of affordance is applied to a robot, performing the whole process of recognition and planning is not always required in its computer. Since the tactile sensing of a robot is important to perform any task, we focus on tactile sensing and introduce a new concept called the artificial tactile affordance system (ATAS). Its basic idea is the implementation of a recurrent mechanism in which
information obtained from the object and the behavior performed by the robot’s inducing the next behavior. We intend to implement ATAS based on the following two methodologies: (1) after each rule is transformed into an algorithm, a program module is coded based on the algorithm; ATAS is composed of several program modules, and a module is selected from the set of modules based on
sensor information; (2) a set of rules is expressed as a table composed of sensor input columns and behavior output columns, and the table rows correspond to rules; since each rule is transformed to a string of 0 and 1, we treat a long string composed of rule strings as a gene to obtain an optimum gene that adapts to its environment using a genetic algorithm (GA). For methodology 1, we established an ATAS composed of 3 to 5 modules to accomplish such tasks as object grasping, pick and place, cap
screwing, and assembling. Using methodology 1, a two-hand-arm robot equipped with an optical threeaxis
tactile sensor performed the above tasks. For methodology 2, we propose the Evolutionary
Behavior Table System (EBTS) that uses a GA to acquire the autonomous cooperation behavior of
multiple mobile robots. In validation experiments, three agents equipped with behavior tables conveyed
an object to a specified goal with higher scores than the four-agent condition. Since the redundant
agent does not interrupt the other agents, the agent acquires the collective behavior of not interrupting
other agents based on its environment information. Methodology 1 is very effective for such fine
control as handling tasks of humanoid robots, and methodology 2 is very useful to obtain general
robotic behavior that is suitable for the environment.

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REFERENCES

[1] J. J. Gibson, “The Ecological Approach to Visual Perception,” Houghton Mifflin Company,1979.
[2] M. Ohka, “Robotic Tactile Sensors,” Encyclopedia of Computer Science and Engineering,Wiley Encyclopedia of Computer Science and Engineering, 5-Volume Set, Editor: Benjamin, W.Wah, pp. 2454 – 2461 (Vol. 4), 2009.
[3] P. H. Winston, “Artificial Intelligence (second edition),” Addison-Wesley, pp. 159-204, 1984.
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[5] Rodney A. Brooks, “Intelligence without representation,” Technical Report MIT AI Lab,1988.
[6] D. E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning,”Addison Wesley, 1989.
[7] M. Ohka, N. Morisawa, and H. B. Yussof, Trajectory Generation of Robotic Fingers Based on Tri-axial Tactile Data for Cap Screwing Task, 2009 IEEE International Conference on Robotics and Automation, pp. 883-888, 2009.
[8] H. Yussof, M. Ohka, M. Yamano, and Y. Nasu, Collision Checking Strategy in Biped Humanoid Robot Navigation Toward Operation in Unseen Environment, Proc. of Tenth IASTED Inter. Conf. Control and Applications, pp. 48-54, 2008.
[9] Valentino Braitenberg, “Adaptation in Natural and Artificial Systems,” MIT Press, 1984.Intelligent Robots and Systems, pp. 1550-1557, 1992.
[13] C. Ronald Kube and Hong Zhang, “Controlling Collective Tasks With An ALN,” IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 289-293, 1993.
[14] Tsutomu Hoshino, Daisuke Mitsumoto, and Tohru Nagano, “Evolution of Robot Behavior and Its Robustness,” Transactions of the Society of Instrument and Control Engineers (Transactions of SICE), Vol. 33, No 6, 533-540, 1997.
[15] H. B. Yussof, J. Wada, and M. Ohka, Object Handling Tasks Based on Active Tactile and Slippage Sensations in Multi-Fingered Humanoid Robot Arm, 2009 IEEE International Conference on Robotics and Automation, pp. 502-507, 2009
[16] M. Ohka, H. Kobayashi, J. Takata, and Y. Mitsuya, An Experimental Optical Three-axis Tactile Sensor Featured with Hemispherical Surface, Journal of Advanced Mechanical Design,Systems, and Manufacturing, Vol. 2, No. 5, pp. 860-873, 2008.
[17] M. Ohka, J. Takata, H. Kobayashi, H. Suzuki, N. Morisawa, and H. B. Yussof, Object Exploration and Manipulation Using a Robotic Finger Equipped with an Optical Three-axis Tactile Sensor, Robotica, Vol. 27, pp. 763-770, 2009.

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