Grid based navigation for autonomous robots-an algorithm based on the integration of vision and sonar data

In this article, visual data obtained by a binocular active vision system is integrated, together with ultrasonic range measurements, in the development of an obstacle detection and avoidance system based on a connectionist grid. This grid based framework allows the integration of two different sensing modalities (vision and sonar) in a way that incoming sensorial data can be mutually enhanced and validated. Each grid node maps a configuration in a discrete subset of the robot´s configuration space. Detected obstacles result in sets of restricted configurations. Using the grid data, a potential field is interactively computed for each mapped configuration. This computation is done in real-time, during the robot´s motion, so the potential field changes as new data is integrated into the grid. The authors also propose the implementation of some innovative sensing and control strategies, using the integrated sensorial information