Avoiding obstacles using a connectionist network

Abstract

In this article, visual data obtained by a binocular active vision system is integrated, together with ultrasonic range measurements, in the development of a obstacle detection and avoidance system based on a connectionist grid. The traditional notion of probabilistic occupation grid is extended through the use of a three-layer structure of connectionist networks which allows the integration of several sensorial modalities (in this case ultrasonic sensor readings and stereo vision information) in a probabilistic environment representation. The connectionist nature of the network also allows us to deal with obstacle avoidance by using a mechanism similar to potential field over a discrete set of the robot's configuration space with each grid node representing a possible configuration. The value in each grid node gives us a measure of the configuration occupancy probability and can also be used to guide the robot to a predefined goal configuration simulating a simple gradient descending technique. Finally we present experimental results obtained with the implementation of the above method in a mobile platform which also provides the support for the sensing devices described throughout the article.