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JRM Vol.20 No.1 pp. 47-60
doi: 10.20965/jrm.2008.p0047
(2008)

Paper:

Virtual Robot Experimentation Platform – A Versatile Small Footprint Robot Simulator

Marc Freese*, Fumio Ozaki**, Shigeo Hirose*,
and Nobuto Matsuhira**

*Department of Mechanical and Aerospace Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan

**Humancentric Laboratory, Corporate R & D Center, Toshiba Corporation, 1 Komukai, Toshiba-cho, Saiwai-ku, Kawasaki-shi, kanagawa 212-8582, Japan

Received:
January 23, 2007
Accepted:
April 5, 2007
Published:
February 20, 2008
Keywords:
robot simulator, proximity sensors, kinematics, path planning
Abstract
This paper presents a small footprint and modular virtual robot experimentation platform capable of easily being integrated into bigger applications. It comes as a library of functions and can be used through a native client application, a script or through an integrated GUI. The platform is organized around calculation modules where each achieves a specific task, namely collision detection, distance calculation, powerful proximity sensor simulation, forward and inverse kinematics, as well as path planning. Additional modules can easily be added and complicated simulations realized by programmatically combining them in a client application. The platform's small size allowed it to be successfully tested and embedded into several applications, as well as to speed-up the design process of a sonar-based navigation and obstacle avoidance algorithm.
Cite this article as:
M. Freese, F. Ozaki, S. Hirose, and N. Matsuhira, “Virtual Robot Experimentation Platform – A Versatile Small Footprint Robot Simulator,” J. Robot. Mechatron., Vol.20 No.1, pp. 47-60, 2008.
Data files:
References
  1. [1] O.Michel, “Cyberbotics Ltd. WebotsTM: professional mobile robot simulation,” Int. J. Adv. Robot. Syst., 1-1, pp. 39-42, 2004.
  2. [2] Y. Nakamura, H. Hirukawa, K. Yamane, S. Kajita, K. Fujiwara, F. Kanehiro, F. Nagashima, Y. Murase, and M. Inaba, “Humanoid Robot Simulator for the METI HRP Project,” J. of Robotics and Autonomous Systems, 37-2, pp. 101-114, 2001.
  3. [3] http://www.mscsoftware.com/
  4. [4] F. Ozaki, J. Oaki, H. Sato, H. Hashimoto, and N. Matsuhira, “Open robot controller architecture (ORCA),” J. Robot. Soc. Japan, 21-6, 2003.
  5. [5] S. Gottschalk, M. C. Lin, and D. Manocha, “OBB-tree : a hierarchical structure for rapid interference detection,” Proc. ACM SIGGRAPH, pp. 171-180, 1996.
  6. [6] J. Arvo and D. Kirk, “A survey of ray tracing acceleration techniques,” In An Introduction to Ray Tracing, Andrew Glassner (Ed.), Academic Press, pp. 201-262, 1989.
  7. [7] D. E. Johnson and E. Cohen, “A framework for efficient minimum distance computations,” Proc. IEEE Int. Conf. Robot. Autom., pp. 3678-3684, 1998.
  8. [8] T. Akenine-Möller, “Fast 3D triangle-box overlap testing,” J. Graphics Tools, 6-1, pp. 29-33, 2001.
  9. [9] T. Möller, “A fast triangle-triangle intersection test,” J. Graphics Tools, 2-2, pp. 25-30, 1997.
  10. [10] S. Quinlan, “Efficient distance computation between non-convex objects,” IEEE Int. Conf. Robot. Autom., pp. 3324-3329, 1994.
  11. [11] X. Tu and D. Terzopoulos, “Perceptual modeling for behavioral animation of fishes,” Proc. 2nd Pacific Conf. Computer Graphics, pp. 165-178, 1994.
  12. [12] O. Renault, N. M. Thalmann, and D. Thalmann, “A vision-based approach to behavioral animation,” J. Visualiz. Computer Anim., 1-1, pp. 18-21, 1990.
  13. [13] J. J. Kuffner Jr., “Autonomous agents for real-time animation,” Ph.D. thesis, Dept. of Computer Science, Stanford University, 1999.
  14. [14] J. H. Clark, “Hierarchical geometric models for Visible surface algorithm,” Communications of the ACM, 19-10, pp. 547-554, 1976.
  15. [15] J.M. Bell, “Application of optical ray tracing techniques to the simulation of sonar images,” Optical Engineering, 36-6, pp. 1806-1813, 1997.
  16. [16] M. Freese, F. Ozaki, and N. Matsuhira, “Collision detection, distance calculation and proximity sensor simulation using oriented bounding box trees,” Proc. Int. Conf. Adv. Mechatron., pp. 13-18, 2004.
  17. [17] R. P. Paul, B. Shimano, and G. E. Mayer, “Kinematic control equations for simple manipulators,” IEEE Trans. Syst., Man, Cybern., SMC-11-6, pp. 449-455, 1981.
  18. [18] D. E. Whitney, “The mathematics of coordinated control of prosthetic arms and manipulators,” Trans. ASME J. Dyn. Syst., Meas., Contr., 94, pp. 303-309, 1972.
  19. [19] A. Balestrino, G. De Maria, and L. Sciavicco, “Robust control of robotic manipulators,” Proc. 9th IFAC World Congr., pp. 80-85, 1984.
  20. [20] W. A. Wolovich and H. Elliott, “A computational technique for inverse kinematics,” Proc. 23rd IEEE Conf. on Decision and Control, pp. 1359-1363, 1984.
  21. [21] C. W. Wampler, “Manipulator inverse kinematic solutions based on vector formulations and damped least squares methods,” IEEE Trans. Syst., Man, Cybern., 16-1, pp. 93-101, 1986.
  22. [22] Y. Nakamura and H. Hanafusa, “Inverse kinematics solutions with singularity robustness for robot manipulator control,” Trans. ASME J. Dyn. Syst., Meas., Contr., 108-2, pp. 163-171, 1986.
  23. [23] L. Wang and C. Chen, “A combined optimization method for solving the inverse kinematics problem of mechanical manipulators,” IEEE Trans. Robot. Autom., 7-4, pp. 489-499, 1991.
  24. [24] M. Vukobratovic and M. Kircanski, “A dynamic approach to nominal trajectory synthesis for redundant manipulators,” IEEE Trans. Syst., Man, Cybern., SMC-14-4, pp. 580-586, 1984.
  25. [25] L. Sciavicco and B. Siciliano, “A solution algorithm to the inverse kinematic problem for redundant manipulators,” IEEE J. Robot. Automat., 4-4, pp. 403-410, 1988.
  26. [26] M. Kircanski and M. Vukobratovic, “Trajectory planning for redundant manipulators in the presence of obstacles,” Proc. 5th CISMIFToMM Ro. Man. Syst., 1984.
  27. [27] A. A. Maciejewski and C. A. Klein, “Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments,” Int. J. Robot. Res., 4-3, pp. 109-117, 1985.
  28. [28] J. Barraquand and J.-C. Latombe, “Robot motion planning : a distributed representation approach,” Int. J. Robot. Res., 10-6, pp. 628-649, 1991.
  29. [29] N. M. Amato and Y. Wu, “A randomized roadmap method for path and manipulation planning,” IEEE Int. Conf. Robot. & Automat., pp. 113-120, 1996.
  30. [30] L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Trans. Robot. & Autom., 12-4, pp. 566-580, 1996.
  31. [31] S. M. LaValle, “Rapidly-exploring random trees: a new tool for path planning,” Comp. Science Dept., Iowa State Univ., 1998.
  32. [32] S. M. LaValle and J. J. Kuffner Jr., “Randomized kinodynamic planning,” Proc. IEEE Int. Conf. Robot. & Automat., pp. 473-479, 1999.
  33. [33] J. J. Kuffner Jr., “RRT-connect: an efficient approach to singlequery path planning,” Proc. IEEE Int. Conf. Robot. & Automat., pp. 995-1001, 2000.
  34. [34] http://www.lua.org/
  35. [35] T. Yoshimi et al., “Development of a concept model of a robotic information home appliance, ApriAlpha,” Proc. IEEE int. Conf. on Intelligent Robots and Syst., pp. 205-211, 2004.
  36. [36] E. F. Fukushima et al., “Teleoperated buggy vehicle and weight balanced arm for mechanization of mine detection and clearance tasks,” Proc. HUDEM2005, pp. 58-63, 2005.
  37. [37] M. Freese, S. P. N. Singh, E. F. Fukushima, and S. Hirose, “Biastolerant terrain following method for a field deployed manipulator,” Proc. IEEE Int. Conf. Robot. & Automat., pp. 175-180, 2006.

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