Control of Sensory Perception for Discrete Event Systems
Geir E. Hovland
Copyright © 1999 Geir E. Hovland
All rights reserved.
Control of Sensory Perception for
Discrete Event Systems
Geir Edvin Hovland
Siv.Ing. (NTNU Norway)
A thesis submitted for the degree of Doctor of Philosophy
of The Australian National University
Department of Engineering
Faculty of Engineering and Information Technology
The Australian National University
About the author
Geir Edvin Hovland was born in Stavanger, Norway, in 1970. He received his MSc
degree in Control Engineering from the Norwegian University of Science and Technol-
ogy, Trondheim, Norway in 1993. His MSc thesis was titled “Passivity based velocity
observer for robot control”. He received his PhD in 1998 from the Engineering Depart-
ment of the Australian National University, Canberra, Australia. Since 1997 he has
been a senior research scientist at ABB Corporate Research in Oslo, Norway and ABB
Robotics in V¨ aster˚ as, Sweden. His current interests include identification and control of
lightweight and elastic industrial manipulators, force-controlled industrial manipulation
and automation of assembly/disassembly. (Contact email: geir email@example.com).
Some of the reviewers comments on the thesis
This thesis is concerned with the application and development of discrete event systems
modeling to the problem of the control of sensory perception. The subject and approach
taken by the candidate is indeed novel and differs substantially in philosophy and intent
from previous approaches to this problem. The work is a significant contribution to
the general area of robotics and sensory control.
The notion of discrete sensing states and transitions between states is what distinguishes
the approach taken in this thesis from the more continuous-time, information-theoretic
methods. The thesis contains significant advances in the state of the art in on-line
monitoring of sensor-based robot actions. The emphasis is on monitoring of contact
changes and estimation of the contact situation after the transitions, during repetitive
1.3. Organisation of the Thesis 5
is difficult. The sensitivity analysis is performed by exploring the relationship
between discounted Markov decision problems and linear programming.
Chapter 5 demonstrates the control of sensory perception for a planar robotic assem-
bly task. The assembly task is modelled as a constrained motion system and
the discrete events correspond to changes in the motion constraints. The con-
trol of sensory perception is demonstrated using four different event recognition
Chapter 6 demonstrates the control of sensory perception in mobile navigation. The
navigation problem is modelled as a discrete event system, where the discrete
events correspond to changes in the mobile unit motion constraints. The sensory
perception problem is demonstrated for a planar model containing three rooms,
fixed walls and open or closed doors.
Chapter 7 brings the conclusions of the thesis. Open problems and areas for further
research are discussed.
The appendices present several process monitors which allow us to demonstrate
the control of sensory perception for robotic assembly and mobile navigation in
Chapter 5 and 6.
Appendix A presents a process monitor based on Hidden Markov Models (HMMs).
Each discrete event is modelled by a HMM which represents a stochastic,
knowledge-based system. The HMMs are trained off-line on planar force/torque
measurements. In real-time operation all HMMs corresponding to possible events
are evaluated. The event with the highest model score is chosen and the associ-
ated confidence level is also calculated from the HMM model scores.
Appendix B presents a process monitor based on position measurements and distance
functions. The monitor is based on a geometrical model of the world and calcu-
lates the nearest distances between all relevant surfaces and edges. The monitor
recognises the world states and calculates confidence levels based on the distance
Appendix C describes a multilayer perceptron process monitor.
trained off-line on force/torque and position measurements. Each network output
corresponds to a discrete event. In real-time operation the measured forces/torques
and positions are used as the network inputs. The event with the highest corre-
sponding network output is chosen and the associated confidence level is calcu-
lated from the network outputs.
The network is
1.3. Organisation of the Thesis6
Appendix D describes a process monitor based on qualitative reasoning. The control
of sensory perception requires each monitor to produce confidence level outputs
of the recognised discrete events. The monitor based on qualitative reasoning was
developed by McCarragher and Asada (1993). The original contribution in this
appendix is the incorporation of confidence levels to the process monitor.
Formulation of the Sensory
Perception Control Problem
2.1 Discrete Event Formalism
The sensory perception control problem addressed in this thesis is formulated in the
discrete event control framework. Ramadge and Wonham (1989) defined a discrete
event system (DES) as a dynamic system that evolves in accordance with the abrupt
occurrence, at possibly unknown irregular intervals, of physical events.
Figure 2.1: Sample path of a discrete event system.
2.1. Discrete Event Formalism8
Figure 2.1 shows a sample path of a discrete event system. The DES state variable,
denoted γ(t), is piece-wise constant and can only change value at the occurrence of a
discrete event. The sensory perception controller (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. The general sensory perception control problem
for discrete event systems is formulated as follows.
tors such as to minimise the cost related to the event recognition error plus the costs of
obtaining the monitor outputs, ie. find the optimal V
Given the occurrence of a discrete event, consult event moni-
V = minf(e∗− e) +
The term?g(ki) represents the cost of obtaining the monitor outputs, where kiare the
individual monitor costs. The individual monitor costs are usually fixed and determined
off-line. Only the event monitors actually consulted by the SPC are contained in the set
I. The term f(e∗−e) represents the cost related to the event recognition error, where
e∗is the recognised event from the SPC. In general, the correct event e is unknown.
Hence, the event recognition error e∗−e is also unknown. In this thesis, each individual
event monitor produces a confidence level C ∈ [0,1] which is used to estimate the event
recognition error. A large confidence level indicates a low recognition error. A trade-
off has to be made between the event recognition errors and the monitor costs. Low
monitor costs often result in large average event recognition errors, while large monitor
costs often result in low average event recognition errors.
Figure 2.2 shows the block diagram of the discrete event control structure. The percep-
tual capabilities of the discrete event system consist of several process monitors. Process
monitor i recognises a discrete event ei(tk) occurring at time tk. Due to noisy measure-
ments, model uncertainties or world unpredictability, the recognised event ei(tk) may
not correspond to the actual physical event e(tk) that occurred. A very important fea-
ture of a process monitor is its ability to indicate the confidence level of the recognised
event. A good process monitor produces low confidence levels for events recognised
incorrectly and large confidence levels for events recognised correctly.
When a discrete event occurs, the sensory perception controller (SPC) has the option
of consulting any of the process monitors. The SPC has two main objectives. First,
the SPC must use the recognised events ei(tk) and the corresponding confidence levels
Ci(tk) efficiently to correctly recognise the actual discrete events. Even when some of