Multisensor fusion for decision-based control cues

Data acquired from multiple sensors provides a means for defining a knowledge base and a current situation scenario. The data is accepted and integrated as intelligence with the use of signal- and symbol-level fusion to translate the raw data into intelligence information that can be used to baseline the knowledge of a control system. An application of this technique is applied to a robotic inspection and dismantlement system. This system is used to dismantle materials in a potentially hazardous environment that involves nuclear waste. The objective is to gather information about the environment using a suite of sensors to include range, electro-optical and proximity sensors to develop a current situation scenario and initiate cues to the control system. By including evidential reasoning in the fusion process, all of the data that is gathered can be used to build the knowledge base where lower belief factors are attributed to things with significant uncertainty. Logical inferences are also incorporated to develop certainty measures and truth values. The results suggest an approach to multisensor fusion for decision-based control using a knowledge base and current situation scenario framework.