Integrating neuromorphic action-oriented perceptual inputs to generate a navigation behaviour for a robot.
We use neural networks with pointer map architectures to provide simple attentional processing in a robotic task. A pointer map comprises a map of neurons that encode a stimulus. Besides global feedback inhibition, the map receives feedback excitation via a small group of pointer neurons that encode the location of a salient stimulus on the map as a vectorial representation. The pointer neurons are able to apply selective processing to a particular region of the network. The robot uses these properties to manoeuver in relation to an attended object. We implemented a controller composed of two pointer maps, and a motor map. The first pointer map reports the direction of a salient obstacle in a one-dimensional map of distance derived from infrared sensors. The second pointer map reports the direction to potential obstacles in a two-dimensional edge-enhanced image derived from a forward looking CCD-camera. These outputs are applied to a motor map, where they bias the motor control signals issued to the robots wheels, according to navigational intentions.
Mudra
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R
Institute of Neuroinformatics, ETH-University Zürich, Switzerland.
Hahnloser
R
R
Douglas
R J
RJ
D001362:Avoidance Learning; D005246:Feedback; D008124:Locomotion; D009046:Motor Neurons / Q000502:physiology; D016571:Neural Networks, Computer; D009434:Neural Pathways; D009475:Neurons, Afferent / Q000502:physiology; D009949:Orientation; D012371:Robotics / Q000295:instrumentation / Q000379:methods*; D013037:Spatial Behavior; D014796:Visual Perception