Title :
A counterpropagation neural network for determining target spacecraft orientation
Author :
Vinz, Bradley L. ; Graves, Sara J.
Author_Institution :
Dept. of Comput. Sci., Alabama Univ., Huntsville, AL, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
This paper describes a concept that integrates a counterpropagation neural network into a video-based vision system employed for automatic spacecraft docking. A brief overview of docking phases, the target orientation problem, and potential benefits resulting from an automated docking system is provided. Issues and challenges of automatic target recognition, as applied to automatic docking, are addressed. Following a review of the architecture, training, and desirable characteristics of the counterpropagation network, an approach for determining the relative orientation of a target spacecraft based on a counterpropagation net is presented
Keywords :
aerospace control; computer vision; feedforward neural nets; learning (artificial intelligence); object recognition; space vehicles; aerospace control; automatic spacecraft docking; automatic target recognition; counterpropagation neural network; target spacecraft orientation; video-based vision system; Artificial neural networks; Automatic control; Cameras; Computer science; Control systems; Layout; Machine vision; Neural networks; Space vehicles; Target recognition;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374965