Control of sensory perception for discrete event systems
Abstract
The problem of controlling sensory perception for use in discrete event feedback control
systems is addressed in this thesis. The sensory perception controller (SPC) is formulated
as a sequential Markov decision problem. The SPC has two main objectives; 1) to
collect perceptual information to identify discrete events with high ·levels of confidence
and 2) to keep the sensing costs low. Several event recognition techniques are available
where each of the event recognisers produces confidence levels of recognised events.
For a discrete event control system running in normal operation, the confidence levels
are typically large and only a few event recognisers are needed. Then, as the event
recognition becomes harder, the confidence levels will decrease and additional event
recognisers are utilised by the SPC. The final product is an intelligent architecture
with the ability to actively control the use of sensory input and perception to achieve
high performance discrete event recognition ..
The discrete event control framework is chosen for several reasons. First, the theory of
discrete event systems is applicable to a wide range of systems. In particular, manufacturing,
robotics, communication networks, transportation systems and logistic systems
all fall within the class of discrete event systems. Second, the dynamics of the sensing
signals used by the event recognisers are often strong and contain a large amount of
information at the occurrence of discrete events. Third, because of the discrete nature
of events, feedback information is not required continuously. Hence, valuable processing
time is available between events. Fourth, the discrete events are a natural common
representational format for the sensors. A common sensor format aids the decision
process when dealing with different sensor types. Fifth, the sensing aspect of discrete
event systems has often been neglected in the literature. In this thesis we present a unique approach to on-line discrete event identification.
The thesis contains both theoretical results and demonstrated real-world applications.
The main theoretical contributions of the thesis are 1) the development of a sensory
perception controller for the dynamic real-time selection of event recognisers. The
proposed solution solves the Markov decision process using stochastic dynamic programming
(SDP). SDP guarantees cost-efficiency of the real-time SPC by solving a
sequential constrained optimisation problem. 2) A sensitivity analysis method for the
sensory perception controller has been developed by exploring the relationship between
Markov decision theory and linear programming. The sensitivity analysis aids in the
robust tuning of the SPC by finding low sensitivity areas for the controller parameters.
Two real-world applications are presented. First, several event recognition techniques
have been developed for a robotic assembly task. Robotic assembly fits particularly well
in the discrete event framework, where discrete events correspond to changes in contact
states between the workpiece and the environment. Force measurements in particular
contain a significant amount of information when the contact state changes. Second, the
sensory perception control theory and the sensitivity analysis have been demonstrated
for a mobile navigation problem. The cost-efficient use of sensory perception reduces
the need for mobile robots to carry heavy computational resources.