A knowledge-based system framework for environmental perception in a subsea robotics context

A knowledge-based system structure capable of providing the information and processing framework required by an unmanned autonomous submersible is presented. The component parts of such a robotic system are first discussed, followed by a description of the information and processing subsystems that are, therefore, required. The framework within which they are implemented uses the architecture of a blackboard system to specify a modular unit (the blackboard cell) capable of manipulating the diverse types of process and uncertain information encountered. In this respect, the cell represents a departure from previously reported blackboard implementations. Interconnection of these modular units uses a standard interface, configured as a globally accessible blackboard. As a means of evaluation, a software implementation of the architecture is applied to the sonar perception subsystem. The image processing tools at the heart of a simple knowledge base are described, and results are presented to show a small sequence of system execution, and some examples of candidate object and shadow areas derived from real sonar images. It is demonstrated that the blackboard cell allows a rich feedback structure to the flow of information and processes, allowing both model and data-driven actions to support the formation of competitive and cooperative solutions.