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Optimal arrest and guidance of a moving prismatic object using multiagents

Published online by Cambridge University Press:  01 January 2008

Pankaj Sharma ,
Anupam Saxena  and
Ashish Dutta
Pankaj Sharma
Affiliation:
Department of Mechanical Engineering, Indian Institute of Technology, Kanpur 208016INDIA.
Anupam Saxena
Affiliation:
Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA. E-mail: [email protected]
Ashish Dutta*
Affiliation:
Department of Mechanical Engineering, Nagoya University, Chikusa-ku, Furo-cho, Nagoya 464-8603, Japan.
*
*Corresponding author: E-mail: [email protected]
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Summary

Genetic algorithm is used to determine the optimal capture points for the multi agents required to grasp a moving generic prismatic object by arresting it in form closure. Thereafter, the agents approach their respective moving goals using a decentralized projective path planning algorithm. Post arrest, the object is guided along a desired linear path to a desired goal point. Form closure of the object is obtained using the concept of accessibility angle. A convex envelop is formed around the object, and the goal points on the object boundary are mapped onto the envelope. The robots approach the mapped goal points first, and then, converge on the actual object. This ensures that the agents reach the actual goal points almost simultaneously, and do not undergo looping at a local concave region. The object is assumed alive while being captured but is assumed compromised thereafter. Post arrest, robots alter their positions optimally around the object to transport it along a desired direction. Frictionless point contact between the object and a robot is assumed. The shape of the mobile robot is considered cylindrical such that it can only apply force along the outward radial direction. Simulation results are presented that illustrate the effectiveness of the proposed method.

Keywords

accessibility anglemulti agentsprojective path planninggenetic algorithm
Type
Article
Information
Robotica , Volume 26 , Issue 1 , January 2008 , pp. 41 - 53
Copyright
Copyright © Cambridge University Press 2007

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References

1.Bicchi, A. and Kumar, V. “Robot Grasping and Contact: A Review,” Proceedings of IEEE International Conference on Robotics and Automation, (2000), pp. 348–353.Google Scholar
2.Yoshikawa, T., “Passive and Active Closures by Constraining Mechanisms,” Proceedings of IEEE International Conference on Robotics and Automation, Minneapolis, Minnesota (Apr. 1996).Google Scholar
3.Rimon, E. and Blake, A., “Caging 2D Bodies by 1-Parameter Two-Fingered Gripping Systems,” Proceedings of IEEE International Conference on Robotics and Automation, Minneapolis, Minnesota (Apr. 1996).Google Scholar
4.Davidson, C. and Blake, A., “Caging Planar Objects with a Three-Finger One-Parameter Gripper,” Proceedings of IEEE International Conference on Robotics and Automation, Leuven, Belgium, (May 1998).Google Scholar
5.Ding, D., Liu, Y., Zhang, J. and Knoll, A., “Computation of Fingertip Positions for a Form-Closure Grasp,” Proceedings of IEEE International Conference on Robotics and Automation, Seoul, Korea. (May 21–26, 2001).Google Scholar
6.Ponce, J. and Faverjon, B., “On computing three-finger force closure grasp of polygonal objects,” IEEE Trans. Robot. Autom., 11 (6), 868–889 (Dec. 1995).CrossRefGoogle Scholar
7.Kaneko, M., Harada, K. and Tsuji, T., “Dynamic Friction Closure,” Proceedings of IEEE International Conference on Robotics and Automation, Washington, DC (May 2002).Google Scholar
8.Aiyama, Y., Hara, Ma., Yabuki, T., Ota, J. and Arai, T., “Cooperative transport by two four legged robot by implicit communication,” Robot. Autonom. Sys. 29, 31–19 (1999).Google Scholar
9.Hashimoto, M., Oba, F. and Zenitani, S., “Object-Transportation Control by Multiple Wheeled Vehicle-Planar Cartesian Manipulator Systems,” Proceedings of IEEE International Conference on Robotics and Automation, (1995) pp. 2267–2272.Google Scholar
10.Hashimoto, M. and Uminoroib, F. A., “Dynamic Control Approach for Motion Coordination of Multiple Wheeled Mobile Robots Transporting a Single Object,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Yokohama, Japan (Jul. 26–30, 1993).Google Scholar
11.Ahmadabadi, M. N. and Nakano, E., “A constrain and move approach to distributed object manipulation,” IEEE Trans. Robot. Autom. 17 (2), 157–172 (Apr. 2001).CrossRefGoogle Scholar
12.Yamada, S. and Saito, J., “Adaptive action selection without explicit communication for multi-robot box-pushing,” IEEE Trans. Syst., Man, Cybern. C: Appl. Rev. 31 (3), 398–404 (Aug. 2001).CrossRefGoogle Scholar
13.Sun, D. and Mills, J. K., “Manipulating rigid payloads with multiple robots using compliant grippers,” IEEE/ASME Trans. Mechatronics 7 (1), 23–34 (Mar. 2002).Google Scholar
14.Galta, C., Lumia, R., Wood, J. and Starr, G., “An Efficient Method to Compute Three Fingered Planar Object Grasps Using Active Contour Models,” Proceedings of IEEE/RSJ International conference on Intelligent Rebote and Systems (Sep. 2004) pp. 3674–3679.Google Scholar
15.Belta, C. and Kumar, V., “Abstraction and control for group of robots,” IEEE Trans. Robot. 20 (5), 865–875 (Oct. 2004).CrossRefGoogle Scholar
16.Burgard, W., Moors, M., Stachniss, C. and Schneider, F. E. “Coordinated multi-robot exploration,” IEEE Trans. Robot., 21 (3), 367–378 (Jun. 2005).CrossRefGoogle Scholar
17.Sugar, T. G. and Kumar, V., “Control of cooperating mobile manipulators,” IEEE Trans. Robot. Autom. 18 (1), 94–103 (Feb. 2002).CrossRefGoogle Scholar
18.Spletzer, J., Das, A. K., Fierro, R., Taylor, C. J., Kumar, V. and Ostrowski, J. P., “Cooperative Localization and Control for Multi-Robot Manipulation,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, (2001) pp. 631–636.Google Scholar
19.Carpin, S. and Parker, L. E., “Cooperative Leader Following in a Distributed Multi-Robot System,” Proceedings of IEEE International Conference on Robotics and Automation, Washington, DC (May 2002) pp. 2994–3001.Google Scholar
20.Fierro, R., Das, A. K., Kumar, V. and Ostrowski, J. P., “Hybrid Control of Formations of Robots,” Proceedings of IEEE International Conference on Robotics and Automation, Seoul, Korea, (May 21–26, 2001) pp. 157–162.Google Scholar
21.Fierro, R. and Das, A. K., “Hybrid Control of Reconfigurable Robot Formations,” Proceedings of the American Control Conference, Denver, Colorado, (Jun 4–6, 2003) pp. 4607–4612.Google Scholar
22.Sudsang, A. and Ponce, J., “A New Approach to Motion Planning for Disc-Shaped Robots Manipulating a Polygonal Object in the Plane,” Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, CA, (Apr. 2000) pp. 1068–1075.Google Scholar
23.Wang, Z. and Kumar, V., “Object Closure and Manipulation by Multiple Cooperating Mobile Robots,” Proceedings of IEEE International Conference on Robotics and Automation, Washington, DC, (May 2002) pp. 394–399.Google Scholar
24.Buttazzo, G. C., Allotta, B. and Fanizza, F. P., “Mousebuster: A robot for real-time catching,” IEEE Control Syst. Mag. 14 (1), 49–56 (Feb. 1994).Google Scholar
25.Liu, Y., Hoover, A. and Walker, I., “A timing model for vision-based control of industrial robots”, IEEE Transactions on robotics 20 (5), 2004, pp. 891–898.CrossRefGoogle Scholar
26.Song, P. and Kumar, V., “A Potential Field Based Approach to Multi-Robot Manipulation,” Proceedings of IEEE International Conference on Robotics and Automation, Washington, DC, (May 2002) pp. 1217–1222.Google Scholar
27.Asada, H. and By, A. B., “Kinematic analysis of work-part fixturing for flexible assembly with automatically reconfigurable fixtures,” IEEE J. Robot. Autom. Ra-1 (2), 86–93 (1985).CrossRefGoogle Scholar
28.Gordy, M., A Matlab Routine for Function Maximization Using Genetic Algorithm. (Matlab Codes. GA, 1996).Google Scholar
29.Sharma, P., Dutta, A. and Saxena, A. “Determination of Optimal Contact Points for Constraining a Prismatic Object by a Group of Mobile Robots,” Proceedings of the IEEE Conference on Robotics and Mechatronics, RAM, Bangkok, (2006) pp. 86–90.Google Scholar
30.Sharma, P., Saxena, A. and Dutta, A., “Multi Agent Form Closure Capture of a Generic 2D Polygonal Object Based on Projective Path Planning,” Proceedings of the ASME 2006 International Design Engineering Technical Conferences, Philadelphia, USA, (Sep., 2006) pp. 1–8.CrossRefGoogle Scholar