Sensory fusion for intelligent navigation of mobile robot

Abstract

We develop techniques to fuse a robot's sonar and wheel encoder information to produce a map. The sonar(s) gives the distance of the closest object(s) to the robot. The wheel encoder gives the current position of the robot from a starting reference position. Because of the physical limitations on the sonar, including poor angular resolution and poor accuracy, one cannot use sonar information directly for localization. The problem with angular resolution could be overcome by taking the reading at multiple view points. The accuracy problem could be reduced by integrating the sonar values over time. Another problem is specular reflections. A specular environment causes the pulses from the sonar to not return to the emitter, so that the object will be invisible to the sonar. A Bayesian probabilistic map is used in conjunction with a pulse coupled neural network icon of the image to locate the place. The network can handle image variations such as translation, rotation, distortion and scaling invariance, and is developed based on visual cortical processing which is suitable for identifying places. They are capable of image smoothening, image segmentation and object classification.