Experience Based Surface Discernment by a Quadruped Robot
Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007
The task of autonomous surface discernment by an AIBO robotic dog is addressed. Different surface textures (plywood board, thin foam, short carpet, shag carpet) as well as different inclines (0 and 10 degrees) are considered. Using a genetic algorithm, gaits are designed which allow the robot to traverse each of these surfaces in an (approximately) optimal fashion. Frequency domain analysis of actuator readings from individual leg joints is performed for data collected using each gait on each surface type. It is found that the spectral content of these signals is significantly dependent on the characteristics of both the gait in use and the surface being walked upon. Using tap-delay Adaline neural networks to integrate actuator readings from 15 independent joints into a set of models of different gait/surface experiences, an algorithm is designed which uses these experiences to yield high classification rates across surface transitions and with low latency. © 2007 IEEE.
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Holmstrom, Lars; Toland, Andrew; and Lendaris, George G., "Experience Based Surface Discernment by a Quadruped Robot" (2007). Systems Science Faculty Publications and Presentations. 207.