Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T02:15:53.642Z Has data issue: false hasContentIssue false

A segmentation algorithm for collision avoidance in telerobotics applications

Published online by Cambridge University Press:  09 March 2009

Abstract

This paper proposes a new algorithm, known as the Segmentation Algorithm, which provides model-based, real-time, whole-arm collision avoidance for telerobotic applications. The work presented here is an extension and modification of potential field theory. Novel aspects of the algorithm include the application of a hierarchical segmentation technique to minimize on-line processing and the development of procedures which account for workspace object translation, rotation, and grasping. The'SA outputs torques, which, when applied to the control arm, prevent the teleoperator from driving the remote arm into a collision. The teleoperator actually feelsworkspace objects that are spatially close to the remote arm—an experience known as virtual force-reflection. The SA's performance has been analyzed in terms of its speed and efficiency vis a vis various system parameters, including workspace object distribution, size, and number. Simulation results show that the SA succeeds in providing real-time collision avoidance where less elegant brute force algorithms fail.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Lumelsky, V.,“On Human Performance in TeleroboticsIEEE Trans, on Sys., Man, and Cybern 21, No. 5, 971982 (09, 1991).CrossRefGoogle Scholar
2.Johnson, E. G., Corliss, W. R., Human Factors in Teleoperator Design and Operation (Wiley and Sons, New York, 1971).Google Scholar
3.Huang, Y.K. and Ahuja, N., “A Potential Field Approach to Path PlanningIEEE Trans, on Robotics and Automation 8, No. 1, 2332 (02, 1992).Google Scholar
4.Lozano-Perez, T., “A Simple Motion-Planning Algorithm for General Robot ManipulatorsIEEE J. Robotics and Automation RA–3, No. 3, 224238 (1987).CrossRefGoogle Scholar
5.Mao, Z. and Hsia, T.C., “Obstacle Avoidance Inverse Kinematics Solution of Redundant Manipulators by Neural NetworksProc. IEEE Int. Conf. Robotics and Automation 3,10141020 (05, 1993).Google Scholar
6.Kim, J. and Kohsla, P.K., “Real-Time Obstacle Avoidance Using Harmonic Potential FunctionsIEEE Trans, on Robotics and Automation 8, No. 3, 338349 (06, 1992).CrossRefGoogle Scholar
7.Volpe, R. and Khosla, P., “Artificial Potential with Elliptical Isopotential Contours for Obstacle Avoidance” Proc. 26th Conf. on Decision and Control(Dec. 1987) pp. 180185.CrossRefGoogle Scholar
8.Khosla, P. and Volpe, R., “Superquadric Artificial Potentials for Obstacle Avoidance and ApproachProc. IEEE Int. Conf.Robotics and Automation 3,17781784 (1988).Google Scholar
9.Khatib, O., “Real-time Obstacle Avoidance for Manipulators ad Mobile Robotics” Proc. IEEE Int. Conf Robotics and Automation(March, 1985) pp. 500505.Google Scholar
10.Cameron, S.A. and Culley, R.K., “Determining the Minimum Translational Distance Between Two Convex PolyhedraProc. IEEE Int. Conf Robotics and Automation 1, 591596 (04 1986).Google Scholar
11.Strenn, S., “Model-Based Collision Avoidance for Telerobotic Applications” Masters Thesis (Department of Electrical and Computer Engineering, University of California at Davis, 06 1993).Google Scholar
12.Shaffer, C.A. and Herb, G.M., “A Real-Time Robot Arm Collision Avoidance SystemIEEE Trans, on Robotics and Automation 8, No. 2, 149160 (04 1992).CrossRefGoogle Scholar