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JRM Vol.22 No.4 pp. 447-455
doi: 10.20965/jrm.2010.p0447
(2010)

Paper:

A Framework to Identify Task-Phase and Attentional-Condition for Supporting Complicated Dual-Arm Operations

Mitsuhiro Kamezaki*, Hiroyasu Iwata**, and Shigeki Sugano***

*Department of Modern Mechanical Engineering, School of Creative Science and Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, Tokyo 162-0044, Japan

**Waseda Institute for Advanced Study (WIAS), Waseda University, 1-6-1 Nishi Waseda, Shinjuku-ku, Tokyo 169-8050, Japan

***Department of Modern Mechanical Engineering, School of Creative Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan

Received:
December 20, 2009
Accepted:
March 29, 2010
Published:
August 20, 2010
Keywords:
state identification, task phase, attentional condition, operational support, construction machinery
Abstract

The state identification framework we propose supports complex construction machinery operations using a dual arm. Such support requires compatibility with different types of support and commonality among various operator skill levels. Our framework is organized into (i) real-time task phase identification defined using joint load applied based on environment constraints and (ii) time-series attentional condition identification defined as an internal work-state condition classified by the operational support necessity level and dependent on the vectorial or time-series data selected by the identified task phase. Experiments are conducted using the instrumented hydraulic dual arm system for transport and removal tasks, including complex dual-arm operations. Results show that the number of erroneous contacts, internal force applied, and mental workload decreased without any increase in time, confirming that operational support based on our framework greatly improves individual operator work performance.

Cite this article as:
Mitsuhiro Kamezaki, Hiroyasu Iwata, and Shigeki Sugano, “A Framework to Identify Task-Phase and Attentional-Condition for Supporting Complicated Dual-Arm Operations,” J. Robot. Mechatron., Vol.22, No.4, pp. 447-455, 2010.
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References
  1. [1] A. Ishii, “Operation System of a Double-Front Work Machine for Simultaneous Operation,” Proc. Int. Symp. Automation and Robotics in Construction, pp. 539-542, 2006.
  2. [2] G. Duchemin, P. Maillet, P. Poignet, E. Dombre, and F. Pierrot, “A Hybrid Position/Force Control Approach for Identification of Deformation Models of Skin and Underlying Tissues,” IEEE Trans. Biomedical Engineering, Vol.52, No.2, pp. 160-170, 2005.
  3. [3] S. Nagiri, Y. Amano, K. Fukui, and S. Doi, “The study of driving support system for individual driver,” Proc. IEEE Int. Conf. Systems, Man and Cybernetics, pp. 563-568, 2002.
  4. [4] M. Kamezaki, H. Iwata, and S. Sugano, “Primitive Static States for Intelligent Operated-Work Machines,” Proc. IEEE Int. Conf. Robotics and Automation, pp. 1334-1339, 2009.
  5. [5] M. Kamezaki, H. Iwata, and S. Sugano, “Operator Support System Based on Primitive Static States in Intelligent Operated Work Machines,” Advance Robotics, Vol.23, No.10, pp. 1821-1297, 2009.
  6. [6] M. Kamezaki, H. Iwata, and S. Sugano, “Development of an operation skill-training simulator for double-front work machine –training effect for house demolition work–,” J. Robotics and Mechatronics, Vol.20, No.4, pp. 602-609, 2008.
  7. [7] S. Skaff, A. Rizzi, H. Choset, and P. C. Lin, “A Context-Based State Estimation Technique for Hybrid Systems,” Proc. IEEE Int. Conf. Robotics and Automation, pp. 3924-3929, 2005.
  8. [8] N. Ishikawa and K. Suzuki, “Development of Human and Robot Collaborative System for Inspecting Patrol of Nuclear Power Plants,” Proc. of IEEE Int. Workshop on Robot and Human Communication, pp. 118-123, 1997.
  9. [9] J. Yang, Y. Xu, and C. S. Chen, “Hidden Markov Model Approach to Skill Learning and Its Application to Telerobotics,” IEEE Trans. Robotics and Automation, Vol.10, No.5, pp. 521-531, 1994.
  10. [10] K. Itabashi, T. Suzuki, and S. Okuma, “Identification of task skill with hidden Markov model,” Technical Report of IEICE, Vol.97, No.156, pp. 73-80, 1997.
  11. [11] S. G. Hart and L. E. Staveland, “Development of NASA-TLX (task load index) results of empirical and theoretical research,” P. A. Hancock & N. Meshkati (eds.), Human Mental Workload, pp. 139-183, 1988.

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Last updated on Feb. 25, 2021