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A neural-network approach to high-precision docking of autonomous vehicles/platforms

Published online by Cambridge University Press:  13 February 2007

Joseph Wong
Affiliation:
Computer Integrated Manufacturing Laboratory, Department of Mechanical and Industrial Engineering University of Toronto, 5 King's College Road, Toronto, M5S 3G8, Ontario, Canada
Goldie Nejat*
Affiliation:
Department of Mechanical Engineering, State University of New York at Stony Brook, Stony Brook, 11794–2300, New York, USA
Robert Fenton
Affiliation:
Computer Integrated Manufacturing Laboratory, Department of Mechanical and Industrial Engineering University of Toronto, 5 King's College Road, Toronto, M5S 3G8, Ontario, Canada
Beno Benhabib
Affiliation:
Computer Integrated Manufacturing Laboratory, Department of Mechanical and Industrial Engineering University of Toronto, 5 King's College Road, Toronto, M5S 3G8, Ontario, Canada
*
*Corresponding author. E-mail: Goldie.Nejat@stonybrook.edu

Summary

In this paper, a Neural-Network- (NN) based guidance methodology is proposed for the high-precision docking of autonomous vehicles/platforms. The novelty of the overall online motion-planning methodology is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation). In such instances, a guidance technique that utilizes Line-of-Sight- (LOS) based task-space sensory feedback is needed to minimize the detrimental impact of accumulated systematic motion errors. Herein, the proposed NN-based guidance methodology is implemented during the final stage of the vehicle's motion (i.e., docking). Systematic motion errors, which are accumulated after a long-range motion are reduced iteratively by executing corrective motion commands generated by the NN until the vehicle achieves its desired pose within random noise limits. The proposed guidance methodology was successfully tested via simulations for a 6-dof (degree-of-freedom) vehicle and via experiments for a 3-dof high-precision planar platform.

Type
Article
Copyright
Copyright © Cambridge University Press 2007

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