Fully autonomous or supervisory controlled guided vehicles that utilise on-board intelligent sensing to determine the vehicle's state, the external world, correlate real time events/objects with mapped knowledge, monitor the vehicle's own system health, and compute dynamically its own control strategy, require the use of a wide range of sensors, and the means to fuse or integrate disparate sensor databases when they refer to the same object. In this review paper we consider a multilevel approach to sensory integration for AGVs; level 1 local positional estimation, level 2 sensory consensus, level 3 sensors fusion, level 4 situation assessment. And consider for statistically rich data sources (such as radar, sonar) Bayesian distributed data fusion, whereas for substantially uncertain events we review fuzzy logic, Dempster-Shafer evidential theories, and finally consider non-monotonic AI methods such as endorsements or explanation based reasoning for dealing with symbolic fusion.
Distributed estimation, inferencing and multi-sensor data fusion for real time supervisory control
Verteiltes Abschätzen, Störungen und Datenzusammenführung von Vielfachsensoren für eine Echtzeit-Überwachungssteuerung
1990
6 Seiten, 1 Bild
Aufsatz (Konferenz)
Englisch
Real-Time Optimization Supervisory Control of HEV Powertrain
British Library Conference Proceedings | 2007
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