Hierarchical Robot Multi-Sensor Data Fusion System
The objective of this paper is to develop a knowledge-based hierarchical paradigm for the effective fusion of multiple sensors into the operation of an intelligent system.
A mathematical model is developed to represent the level of confidence measures for determining the optimal fused sensor data. The proposed approach permits data to be merged in both a low-level way (to minimize the influence of noisy data) and a high level way (constraints are put on the influence between sensors and the way the data is combined). This work can potentially clear the air on a number of issues such as the usefulness of faulty sensor isolation, the accuracy of combined data sources, and the overall cohesiveness of the various templates for phased sensing.1
The investigation is based on a Unimation PUMA 560 robot and various external sensors. These include overhead vision, eye-in-hand vision, proximity, tactile array, position, force/torque, cross-fire, overload and slip sensing devices. The efficient fusion of data from different sources will enable the machine to respond promptly in dealing with the “real world.” Towards this goal, the general paradigm of a sensor data fusion system has been developed, and some simulation results for the concepts of sensor data fusion have been demonstrated.
KeywordsCovariance Radar Estima
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