Page 76 - Dynamic Vision for Perception and Control of Motion

3 Subjects and Subject Classes
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3.1 General Introduction: Perception – Action Cycles

Remember the definition of control variables given in the previous chapter: They
encompass all variables describing the dynamic process, which can be changed at
any moment. Usually, it is assumed as an idealization that a mental or computa-
tional decision for a control variable can be implemented without time delay and
distortion of the time history intended. This may require high gains in the imple-
mentation chain. In addition, fast control actuation relative to slow body motion
capabilities may be considered instantaneous without making too large an error. If
these real-world effects cannot be neglected, these processes have to be modeled
by additional components in the dynamic system and taken into account by in-
creasing the order of the model.
The same is true for the sensory devices transducing real-world state variables
into representations on the information processing level. Situation assessment and
control decision-making then are computational activities on the information proc-
essing level in which measured data are combined with stored background knowl-
edge to arrive at an optimal (or sufficiently good) control output. The quality of re-
alization of this desired control and the performance level achieved in the mission
context may be monitored and stored to allow us to detect discrepancies between
the mental models used and the real-world processes observed. The motion-state of
the vehicle’s body is an essential part of the situation given, since both the quality
of measurement data intake and control output may depend on this state.
Therefore, the closed loop of perception, situation assessment/decision–making
and control activation of a moving vehicle always has to be considered in conjunc-
tion. Potential behavioral capabilities of subjects can thus be classified by first
looking at the capabilities in each of these categories separately and then by stating
which of these capabilities may be combined to allow more complex maneuvering
and mission performance. All of this is not considered a sequence of quasi-static
states of the subject that can be changed in no time (as has often been done in con-
ventional AI). Rather, it has to be understood as a dynamic process with alternating
smooth phases of control output and sudden changes in behavioral mode due to
some (external or internal) event. Spatiotemporal aspects predominate in all phases
of this process.

3.2 A Framework for Capabilities

To link image sequences to understanding motion processes in the real world, a
few basic properties of control application are mentioned here. Even though con-
trol variables, by definition, can be changed arbitrarily from one time to the next,
for energy and comfort reasons one can expect sequences of smooth behaviors. For
the same reason, it can even be expected that there are optimal sequences of con-
trol application (however “optimal” is defined) which occur more often than oth-
ers. These stereotypical time histories for achieving some state transition effi-
ciently constitute valuable knowledge not only for controlling movements of the
vehicle’s body, but also for understanding motion behavior of other subjects. Hu-