Dickmanns 1992a : The expectation-based 4D approach
to dynamic machine vision exploiting integral spatio-termporal
models of objects in the real world is discussed in the application domain of
unmanned ground and air vehicles. The method has demonstrated superior
performance over the last half decade in autonomous road vehicle guidance with
three different vans and busses , with an AGV on the
factory floor and with completely autonomous relative state estimation for a
twin turboprop aircraft in the landing approach to a runway without any
external support; in all application areas only a small set of conventional
microcomputers was sufficient for realizing the system. This shows the
computational efficiency of the method combining both conventional engineering
type algorithms and artificial intelligence components in a well balanced way.
The modularity of the approach is demonstrated in a simulation set-up serving both the ground- and the air vehicle applications. Experimental results in both areas are discussed.