This paper presents an evaluation of the maneuverability of impedance-controlled robot motion during a human-robot cooperative positioning task. The objectives of this study are to reveal the results of a quantitative evaluation of the maneuverability of robot motion and to investigate the relationship between the results of the quantitative evaluation and an operator’s higher-order brain activity. Control strategies for the robot that are adequate for human-robot interaction have not yet been explicitly determined because of the difficulty in evaluating the maneuverability of robot motion. First, we analyzed the time normalized position and force/torque trajectories to reveal the characteristics of human motion and performed subjective evaluations for three types of impedance-controlled robot motion, which were controlled using the following strategies: (i) ordinary impedance control, (ii) impedance control with virtual Coulomb friction involved in the robot motion, and (iii) impedance control with a trajectory guidance force. Second, to confirm the analysis results based on the observed trajectories, we investigated differences in the operator’s higher-order brain activity when using the different control strategies by using a functional near-infrared spectroscopy system. The experimental results confirmed the relationship between the analysis results of the control strategies, the motion of the operator, and higher-order brain activity. Consequently, the investigation conducted in this study is effective for evaluating the maneuverability of robot motion during a human-robot cooperative task.

1. [1] H. Kazerooni, “Human-Robot Interaction via the Transfer of Power and Information Signals,” IEEE Trans. on Systems, Man and Cybernetics, Vol.20, No.2, pp. 450-463, 1990.
2. [2] K. Kosuge, Y. Fujisawa, and T. Fukuda, “Control of Mechanical System with Man-Machine Interaction,” Proc. IEEE/RSJ Int. Symposium on Intelligent Robotics and Systems, pp. 87-92, 1992.
3. [3] Y. Hirata, Z. Wang, K. Fukaya, and K. Kosuge, “Transporting an Object by a Passive Mobile Robot with Servo Brakes in Cooperation with a Human,” Advanced Robotics, Vol.23, No.4, pp. 387-404, 2012.
4. [4] R. Ikeura and H. Inooka, “Variable impedance control of a robot for cooperation with a human,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 3097-3102, 1995.
5. [5] T. Takubo, H. Arai, Y. Hayashibara, and K. Tanie, “Human-Robot Cooperative Manipulation Using a Virtual Nonholonomic Constraint,” The Int. J. of Robotics Research, Vol.21, No.5-6, pp. 541-553, 2002.
6. [6] T. Tsumugiwa, Y. Fuchikami, A. Kamiyoshi, R. Yokogawa, and K. Yoshida, “Stability Analysis for Impedance Control of Robot in Human-Robot Cooperative Task System,” J. of Advanced Mechanical Design Systems and Manufacturing, Vol.1, No.1, pp. 113-121, 2007.
7. [7] T. Tsumugiwa, R. Yokogawa, and K. Hara, “Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 644-650, 2002.
8. [8] F. Dimeas and N. Aspragathos, “Fuzzy learning variable admittance control for human-robot cooperation,” Proc. IEEE Int. Conf. on Intelligent Robots and Systems, pp. 4770-4775, 2014.
9. [9] F. Ficuciello, L. Villani, and B. Siciliano, “Variable Impedance Control of Redundant Manipulators for Intuitive Human-Robot Physical Interaction,” IEEE Trans. on Robotics, Vol.31, No.4, pp. 850-863, 2015.
10. [10] N. Takesue, R. Kikuuwe, A. Sano, H. Mochiyama, and H. Fuji- moto, “Tracking assist system using virtual friction field,” Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 3927-3932, 2005.
11. [11] R. Kikuuwe, N. Takesue, A. Sano, H. Mochiyama, and H. Fujimoto, “Admittance and Impedance Representations of Friction Based on Implicit Euler Integration,” IEEE Trans. on Robotics, Vol.22, No.6, pp. 1176-1188, 2006.
12. [12] R. Kikuuwe, T. Yamamoto, and H. Fujimoto, “A Guideline for Low-Force Robotic Guidance for Enhancing Human Performance of Positioning and Trajectory Tracking: It Should Be Stiff and Appropriately Slow,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, Vol.38, No.4, pp. 945-957, 2008.
13. [13] M. Rahman, R. Ikeura, and K. Mizutani, “Investigation of the Impedance Characteristic of Human Arm for Development of Robots to Cooperate with Humans,” JSME Int. J. Series C Mechanical Systems, Machine Elements and Manufacturing, Vol.45, No.2, pp. 510-518, 2002.
14. [14] T. Tsuji and Y. Tanaka, “Tracking control properties of human-robotic systems based on impedance control,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, Vol.35, No.4, pp. 523-535, 2005.
15. [15] M. Cifreka, V. Medvedb, S. Tonkovića, and S. Ostojića, “Surface EMG based muscle fatigue evaluation in biomechanics,” Clinical Biomechanics, Vol.24, Issue 4, pp. 327-340, 2009.
16. [16] T. Kanda, H. Ishiguro, and T. Ishida, “Psychological analysis on human-robot interaction,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 4166-4173, 2001.
17. [17] S. Tharin and A. Golby, “Functional brain mapping and its applications to neurosurgery,” Neurosurgery, Vol.60, No.4-2, pp. 185-201, 2007. DOI: 10.1227/01.NEU.0000255386.95464.52
18. [18] S. P. Ahlfors, G. V. Simpson, A. M. Dale, J. W. Belliveau, A. K. Liu, A. Korvenoja, J. Virtanen, M. Huotilainen, R. B. H. Tootell, H. J. Aronen, and R. J. Ilmoniemi, “Spatiotemporal Activity of a Cortical Network for Processing Visual Motion Revealed by MEG and fMRI,” J. of Neurophysiology, Vol.82, No.5, pp. 2545-2555, 1999.
19. [19] S. Waldert, H. Preissl, E. Demandt, C. Braun, N. Birbaumer, A. Aertsen, and C. Mehring, “Hand Movement Direction Decoded from MEG and EEG,” J. of Neuroscience, Vol.28, No.4, pp. 1000-1008, 2008.
20. [20] T. Yoshikawa, “Manipulability of Robotic Mechanisms,” The Int. J. of Robotics Research, Vol.4, No.2, pp. 3-9, 1985.
21. [21] S. K. Kundu, A. Yamamoto, M. Hara, and T. Higuchi, “Estimation of Human Operational Feeling Level for a Lever Manipulation Task Using Shoulder Angle and Manipulability,” Proc. IEEE Int. Conf. on Systems Man and Cybernetics, pp. 1918-1924, 2010.
22. [22] D. Karnopp, “Computer simulation of stick-slip friction in mechanical dynamic systems,” Trans. of ASME: J. of Dynamic Systems, Measurement, and Control, Vol.107, No.1, pp. 100-103, 1985.
23. [23] H. Igarashi, “Subliminal Calibration for Machine Operation,” Remote and Telerobotics, N. Mollet (Ed.): InTech, pp. 155-170, 2010.
24. [24] T. Flash and N. Hogan, “The Coordination of Arm Movements: An Experimentally Confirmed Mathematical Model,” The J. of Neuroscience, Vol.5, No.7, pp. 1688-1703, 1985.
25. [25] B. Corteville, E. Aertbelien, H. Bruyninckx, J. D. Schutter, and H. V. Brussel, “Human-Inspired Robot Assistant for Fast Point-to-Point Movements,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 3639-3644, 2007.
26. [26] M. Rahman, R. Ikeura, and K. Mizutani, “Control characteristics of two humans in cooperative task and its application to robot control,” 26th Annual Conf. of the IEEE Industrial Electronics Society (IECON 2000), pp. 1773-1778, 2000.
27. [27] R. Ikeura, H. Inooka, and K. Mizutani, “Subjective Evaluation for Maneuverability of a Robot Cooperating with Humans,” J. of Robotics and Mechatronics, Vol.14 No.5, pp. 514-519, 2002. DOI: 10.20965/jrm.2002.p0514
28. [28] Y. Uno, M. Kawato, and R. Suzuki, “Formation and control of optimal trajectory in human multijoint arm movement. Minimum torque-change model,” Biological Cybernetics, Vol.61, No.2, pp. 89-101, 1989.
29. [29] A. Maki, Y. Yamashita, Y. Ito, E. Watanabe, Y. Mayanagi, and H. Koizumi, “Spatial and temporal analysis of human motor activity using noninvasive NIR topography,” Medical Physics, Vol.22, No.12, pp. 1997-2005, 1995.
30. [30] Y. Yamashita, A. Maki, Y. Ito, E. Watanabe, Y. Mayanagi, and H. Koizumi, “Noninvasive near-infrared topography of human brain activity using intensity modulation spectroscopy,” Optical Engineering, Vol.35, Issue 4, pp. 1046-1049, 1996.
31. [31] E. Watanabe, Y. Yamashita, A. Maki, Y. Ito, and H. Koizumi, “Non-invasive functional mapping with multi-channel near infra-red spectroscopic topography in humans,” Neuroscience Letters, Vol.205, No.1, pp. 41-44, 1996.
32. [32] K. Shibuya, T. Sadamotoa, K. Satoa, M. Moriyama, and M. Iwadate, “Quantification of delayed oxygenation in ipsilateral primary motor cortex compared with contralateral side during a unimanual dominant-hand motor task using near-infrared spectroscopy,” BRAIN RESEARCH, pp. 142-147, 2008.
33. [33] T. H. Dai, J. Z. Liu, V. Sahgal, R. W. Brown, and G. H. Yue, “Relationship between muscle output and functional MRI-measured brain activation,” Experimental Brain Research, Vol.140, No.3, pp. 290-300, 2001.
34. [34] M. Okamoto, H. Dana, K. Sakamoto, K. Takeo, K. Shimizu, S. Kohno, I. Oda, S. Isobe, T. Suzuki, K. Kohyama, and I. Dan, “Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping,” Neuroimage, Vol.21, No.1, pp. 99-111, 2004.
35. [35] G. Derosiere, F. Alexandre, N. Bourdillon, K. Mandrick, T. E. Ward, and S. Perrey, “Similar scaling of contralateral and ipsilateral cortical responses during graded unimanual force generation,” NeuroImage, pp. 471-477, 2014.
36. [36] M. Brus-Ramer, J. B. Carmel, and J. H. Martin, “Motor Cortex Bilateral Motor Representation Depends on Subcortical and Interhemispheric Interactions,” The J. of Neuroscience, Vol.29, No.19, pp. 6196-6206, 2009.
37. [37] J. Volkmann, A. Schnitzler, O. W. Witte, and H. Freund, “Handedness and Asymmetry of Hand Representation in Human Motor Cortex,” J. of Neurophysiology, Vol.79, No.4, pp. 2149-2154, 1998.