We present a neural-network method to recalibrate automatically a commercial robot after undergoing wear or damage, which works on top of the nominal inverse kinematics embedded in its controller. Our starting point has been the work of Ritter et al. (1989, 1992) on the use of extended self-organizing maps to learn the whole inverse kinematics mapping from scratch. Besides adapting their approach to learning only the deviations from the nominal kinematics, we have introduced several modifications to improve the cooperation between neurons. These modifications not only speed up learning by two orders of magnitude, but also produce some desirable side effects, like parameter stability. After extensive experimentation through simulation, the recalibration system has been installed in the REIS robot included in the space-station mock-up at Daimler-Benz Aerospace. Tests performed in this set-up have been constrained by the need to preserve robot integrity, but the results have been concordant with those predicted through simulation.
Self-calibration of a space robot
Wie ein mobiler Roboter in einer Weltraumstation sich nach einer Kollision mittels eines neuronalen Netzes selber eicht
IEEE Transactions on Neural Networks ; 8 , 4 ; 951-963
1997
13 Seiten, 12 Quellen
Aufsatz (Zeitschrift)
Englisch
Simultaneous localization and odometry self calibration for mobile robot
British Library Online Contents | 2007
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