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Experiments Suggest
the Possibility of Achieving
Autonomous Robotic
Excavation in Moving Towards
Construction Automation
C
onstruction is of prime economic significance to
many industry sectors. Intense competition,
shortages of skilled labor, and technological ad-
vances are forcing rapid change in the construc-
tion industry, thus motivating construction
automation [1]. Earthmoving machines, such as bulldozers,
wheel loaders, excavators, scrapers, and graders, are common
in construction. Excavation-
based operations are used in gen
-
eral earthmoving, digging, and
sheet-piling for displacing large
amounts of material. On a smaller
scale, operations such as trenching and footing formation re
-
quire precisely controlled excavation. Although the fully au
-
tomated construction site is still a dream of some civil
engineers, research developments have shown the promise of
robotics and automation in construction [2]. Despite the ap
-
parent economic importance of excavation in construction,
there have been few implementations of autonomous or
teleoperated excavators.
A number of researchers have investigated the feasibility of
automating excavation. Many of these studies have addressed
the possible use of autonomous excavators during unmanned
phases of establishing manned Lunar or Martian research sta
-
tions [3], [4]. Much of the work on terrestrial excavation has fo
-
cused on teleoperation, rather than on the system requirements
for autonomous operation. Although there have been a number
of valuable theoretical and experimental contributions to the
field of autonomous, robotic or teleoperated excavation [5]-[8],
autonomous operation of a full-scale excavator has not been
commercially demonstrated.
Many of the experimental studies reported in the literature in-
volve using conventional industrial robots fitted with buckets to
excavate in a bed of loose sand. While there are parallels between
“classical” robotics and robotic excavation, there are also some
pronounced differences. In particular, an excavator is not fixed
relative to the work piece; it plastic
-
ally deforms the work piece by ap
-
plying large forces and is caused to
move relative to the soil by the same
large forces. Furthermore, strategic
and bucket trajectory planning must necessarily occur in a dy
-
namic environment; if the excavator is not changing the profile
of the soil being worked, it is not doing useful work.
This article presents some results of the autonomous exca
-
vation project conducted at the Australian Centre for Field
Robotics (ACFR) with a focus on construction automation.
The application of robotic technology and computer control
is one key to construction industry automation. Excavation
automation is a multidisciplinary task, encompassing a broad
area of research and development
◆
planning
◆
monitoring
◆
environment sensing and modeling
◆
navigation
◆
machine modeling and control.
The ultimate goal of the ACFR excavation project is to dem
-
onstrate fully autonomous execution of excavation tasks in
MARCH 2002
IEEE Robotics & Automation Magazine
20
BY QUANG HA, MIGUEL SANTOS,
QUANG NGUYEN, DAVID RYE,
AND HUGH DURRANT-WHYTE
Robotic Excavation in
Construction Automation
1998 DIGITAL STOCK CORP.
1070-9932/02/$17.00©2002IEEE
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common construction, such as loading a truck or digging a
trench. A number of difficult theoretical and practical prob
-
lems must be solved to achieve this objective. The problems
fall into three main groups: excavation planning, sensing and
estimation, and control.
Experimental Robotic Excavator
A number of experimental studies of robotic excavation [4],
[6], [7] have been conducted by using a conventional indus
-
trial robot fitted with a bucket as the end-effector. Although
this approach has yielded valuable information, we are firmly
of the belief that typical “excavator technology” should be
used to develop autonomous excavation capabilities. Conse
-
quently, we have used a Komatsu PC05-7 hydraulic mini-ex
-
cavator as the basis for our experimental work. This 1.5-tonne
machine has been extensively modified [9] to meet the re
-
quirements of the research and development project. The ex
-
perimental excavator has eight hydraulic actuators: right and
left travel motors, a cab slew motor, and two-way hydraulic
cylinders on the boom swing, boom, dipper arm, bucket, and
back-fill blade axes. The excavator cabin and all operating le
-
vers have been removed and the original manually actuated
direction control valves replaced by electrohydraulic
servovalves. Ancillary equipment added to support the
servovalves includes an accumulator with an unloading valve,
solenoid check valves, and an oil-to-air radiator. Fig. 1 shows
the modified robotic excavator during a trench-forming task,
while the electrohydraulic servovalves, accumulator, and oil
radiator can be seen in Fig. 2. The excavator is extensively in-
strumented with joint angle encoders, pressure transducers,
and load pins. Control is achieved through Moog proprietary
digital controllers in conjunction with an IBM-compatible in-
dustrial PC. The system is fully self-contained, with power de-
rived from the excavator’s electrical system.
Sensing and Estimation
When operating an excavator, a human uses senses such as sight
and hearing together with reasoning based on knowledge and
experience to control and monitor the digging process. In ro
-
botic excavation, sensing, modeling, and decision-making
hardware and software must be used in lieu of the operator.
A number of vehicle and environment sensors are fitted to
the experimental robotic excavator. The hydraulic system is
instrumented with transducers that measure the actuator pres
-
sures and the valve spool positions. Strain-gauge force sensors
enable direct force measurement during digging. Sensing of
the machine’s external environment is essential for planning
and controlling platform motion and autonomous digging op
-
erations and for monitoring progress towards task completion.
A commercial time-of-flight laser measurement system is used
to scan the terrain on either side of the bucket, providing a
surface profile with 10-mm resolution and a statistical error of
± 15 mm within a sensing range of 1-8 m.
Parameter estimation and system identification is an impor
-
tant consideration in robotic excavation. Although a number
of states in the control state space are directly measured by ma
-
chine sensors, estimation of some inaccessible states is required
for control and monitoring purposes. Furthermore, there is
also a need to estimate the uncertain forces arising from inter
-
action between the bucket and the soil, and other external dis
-
turbances such as friction and load inertia changes, in order to
reject their influence and achieve robust performance. A vari
-
ety of robust estimation techniques can be used. In general,
the term “robust estimation” refers to the design of determin
-
istic observers in the presence of system uncertainties, delays,
modeling errors, disturbances, and other unknown factors.
Observers based on variable structure systems [10] are of inter
-
est, as they can provide robust estimations. A variable structure
systems approach to friction estimation and compensation [11]
has been proposed and applied to electrohydraulic servo sys
-
tems [12] for external disturbance rejection in force and posi
-
tion control of the robotic excavator. This approach is
summarized here.
Assuming that the disturbance force is slowly time-varying,
the state model for 1-D motion occasioned by an actuator
force
w
can be written as
&
,
&
,
&
vwF F yv
LL
=− = =0
, (1)
MARCH 2002
IEEE Robotics & Automation Magazine
21
Figure 1. The robotic excavator in a trench-forming task.
Figure 2. A view of the electrohydraulic servovalve stack.
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where
y
is the displacement,
v
is the velocity, and
F
L
is the
external disturbance force to be estimated. A schematic dia
-
gram of the observer proposed for (1) is shown in Fig. 3. In
that figure, a circumflex denotes an estimated quantity,
σµ= sgn()e
y
with
µ>0
,
eyy
y
=−
$
is the output error, and
l
1
and
l
2
are the observer gains. To reduce chattering associated
with the sliding mode of the output error, the signum func
-
tion
sgn( )e
y
is replaced by a sigmoidal expression resulted
from fuzzy reasoning:
σµ γ= tanh( )e
ye
. (2)
The term
γ
e
is a positive constant that determines the rate
of convergence and allows for chatter reduction as explained
in [13].
Lower Level Control
The usual task of a backhoe excavator is to loosen and re-
move material from its original location and transfer it to an-
other location by lowering the bucket, digging by dragging
the bucket through the soil, then lifting, slewing, and dump-
ing the bucket load. In moving towards automatic excava-
tion, there is a need for the development of controllers that
are robust to uncertainties associated with these operations
[9]. Kinematic and dynamic models of excavators are devel
-
oped in [14]-[16]. These models assume that the hydraulic
actuators act as infinitely powerful force sources. Bucket po
-
sition control using a conventional proportional and deriva
-
tive controller is used in [16], [17] for simulation of the dig
-
ging process with limited soil interactions. Rather than
tracking desired position or force trajectories, interaction
control seeks to regulate the relationship between the
end-effector position and the force exerted by the bucket on
the soil. Impedance control has been proposed [8], [18] as
providing a unified approach to both unconstrained and
force-constrained motion of an excavator arm.
In our approach, a robust sliding mode control technique is
developed to implement impedance control for an excavator
using generalized excavator dynamics. The bucket tip is con
-
trolled to track a desired digging trajectory in the presence of
environment and system parameter uncertainties. As a result
of the impedance control strategy, both the piston position
and the ram force of each hydraulic cylinder that are required
to exert a given bucket force at a particular position in world
space can be determined. The problem is then to find the con
-
trol voltages that must be applied to the servovalves to track
these desired commands.
The dynamic model of the excavator can be expressed con
-
cisely in the form of the well-known manipulator equations of
motion [16]
DC BG FT()
&&
(,
&
)
&
(
&
)()() (,) ⌫+++=−
Ltn
FF
,
(3)
where
=[]θθθ
234
T
is the vector of measured joint angles
as defined in Fig. 4; matrix
D()
represents inertia, and matrix
C(,
&
)
represents Coriolis and centripetal effects; vectors
B(
&
)
,
G()
, and
⌫()
respectively represent friction, gravity forces,
and functions of the moment arms;
T
L
represents the load
torques as functions of the tangential and normal components
F
t
and
F
n
of the soil reaction force at the bucket, and
F
is a
vector of the ram forces of the hydraulic actuators that pro
-
duce the torques acting on the joints. The tangential compo
-
nent
F
t
of the total soil resistance force is parallel to the
digging direction. The total resistance is
considered to be the sum of soil resistance
to cutting, friction between the bucket
and the ground, the force required to ac
-
celerate the cut prism of soil, and the force
to cause soil movement within the
bucket. According to [19], the tangential
component can be calculated as
Fkbh
t
=
1
, (4)
where
k
1
is the specific digging force in
Nm
2−
, and
h
and
b
are the thickness and
width of the cut slice of soil. The normal
component
F
n
is calculated as
FF
nt
=ψ
, (5)
IEEE Robotics & Automation Magazine
22
MARCH 2002
y
w
−
e
y
l
1
l
2
Observer
F
L
^
v
σ
µ
−
1
s
−
1
s
1
s
∧
Figure 3. Observer schematic diagram.
Although the fully automated
construction site is still a dream of
some civil engineers, research
developments have shown the
promise of robotics and
automation in construction.
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where
ψ= −01 045..
is a dimensionless
factor that depends on the digging angle,
digging conditions, and the wear of the
cutting edge. For a comprehensive de
-
scription of (3), see [16]. By systemati
-
cally assigning Cartesian coordinate
frames
{}Oxyz
1 111
,
{}Oxyz
2 222
,
{}Oxyz
3 333
, and
{}Oxyz
4 444
,as
shown in Fig. 4, (3) can be cast in Carte
-
sian space as
H(x)x C (x x x B (x G (x)
xxx
&&
,
&
)
&&
)+++
=−=−
−
JFFuF
T
ee
⌫ ,
(6)
where
J
is the Jacobian matrix,
HJDJ
CJ(CDJJJ
GJG
BJB
T1
x
T11
x
T
x
T
=
=−
=
=
−−
−−−
−
−
,
&
),
,
,
and
uJ F
T
=
−
⌫
is a control vector of generalized forces exerted
at
O
4
. The generalized forces of interaction between the
bucket and the soil are
FJT
e
T
=
−
L
, with force entries for the
coordinates (
xz
44
,
) and a torque entry around
y
4
.
Let
x
r
t()
be the desired trajectory of the bucket tip.
Typically, the target impedance is chosen as a linear sec-
ond-order system to mimic mass-spring-damper dynamics
Ze M B Ke
Me Be Ke e
tP t t tP
tP tP tP F
sss() ( )
&& &
,
=++
=++=
2
(7)
where
s
is the derivative operator, and the constant posi
-
tive-definite
nn×
target matrices
M
t
,
B
t
and
K
t
are the matri
-
ces of desired inertia, damping, and stiffness. The position
error
e
P
and the force error
e
F
are defined as
exx
Pr
=−
,
eF F
eFr
=−−()
,
(8)
where
FMxBxKx
rtrtrtr
t()
&& &
=++
is the force set-point. The
control problem is to drive the system state so as to implement
asymptotically the relationship (7), even in the presence of un
-
certainty. If the position error
e
P
approaches zero, the force er
-
ror
e
F
also approaches zero, and vice versa, according to the
specified dynamic relationship defined by the numeric values of
the target matrices
M
t
,
B
t
and
K
t
in (7). Let us define the slid
-
ing functions
sxx x=[ ( ), ( ),..., ( )]ss s
n
T
12
as follows [20]
se MBe MKe M e xx=− − − + = −
−− −
∫∫
&&&
PttPttP tF s
dd
11 1
ττ
(9)
where
&&
xxMBeMKe Me
sr
=+ + −
−− −
∫∫
ttP t t P t F
dd
11 1
ττ
.
(10)
The control input proposed [18] is
uuQ s Ks=− −
$
()sgn
, (11)
where
$
$
&&
$
&
$$
[ ( ),..., (
uH Cx G F
Q
sxs xe,
=+ ++
=
x
Qs Qs
nn11
sgn sgn
()[]
)] , ,
[ ( )],
exp ,
max
T
ii
ii
ii ii
Q
Ks
KK s
>
=
=−−
β
δ
K diag
1
and where
β
i
,
K
i max
, and
δ
i
(
in=12, ,...,
) are some positive
constants to be determined during design.
By using a trigonometric mapping [21] between a joint an
-
gle
θ
i
,
i = 234,,
, and the linear displacement
y
i
of the associated
hydraulic cylinder, the cylinder positions
y =[]yyy
T
234
required to position the bucket tip can be determined. Using
the impedance control action
u
in (6), together with the trans
-
formation between excavator joint space and Cartesian space,
the ram forces
F
of the machine hydraulic actuators can also be
determined. The vectors
y
and
F
then form the reference inputs
to the excavator axis control system.
The actuators driving the boom swing, boom, dipper arm,
and bucket attachments of the excavator are axial hydraulic
cylinders, and the flow of hydraulic oil to each cylinder is reg
-
ulated by an electrohydraulic servovalve. For simplicity, the
following linear expression can be used with little loss of accu
-
racy for frequencies up to 200 Hz
IEEE Robotics & Automation Magazine
23
MARCH 2002
z
0
O
0
O
1
x
0
x
1
x
4
y
4
x
3
O
4
O
3
y
3
Bucket
O
2
y
2
x
2
Boom
y
1
Arm
θ
1
θ
2
θ
3
θ
4
Figure 4. Excavator joint variables assigned using the Denavit-Hartenberg convention.
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xKu
vvv
=
, (12)
where
x
v
is the servovalve spool displacement, and
u
v
is the
valve input voltage. By assigning each hydraulic cylinder force
F to be a state variable, a model for electrohydraulic systems
that actuate machine axes can be obtained [12] as
&
&
[]
&
&
yv
v
M
FF
FavaFaP bu
Pava
L
v
=
=−
=+ + +
=+
1
12 13 14 1 1
122 23
FaP bu
v
++
24 1 2
,
(13)
where
P
1
is the piston head-side fluid pressure, and
a
ij
and
b
i
are time-varying coefficients with nominal values
a
ij
and
b
i
.
The piston velocity
v
and load disturbance
F
L
are estimated
using the observer shown in Fig. 3.
Let us define control errors
eyy evv
eFF ePP
rr
rr
12
3 411
=− =−
=− = −
,,
,,
(14)
where
v
r
and
P
r1
are respectively the desired piston velocity
and the head-side pressure. The following nonlinear dynamics
can be derived for the error vector
e =[]eeee
T
1234
&
(, ) ( )eAxx B=++
r
uf
, (15)
where
Ax x(, )
$
$
&
$
$
r
r
L
r
L
vv e
FF
M
v
eF
M
av aF aP
=
−=
−
−=
−
++
2
3
12 13 14 1
−
++ −
&
$
&
F
avaF aP P
r
r22 23 24 1 1
,
B =
0
0
1
2
b
b
,
and
f
encapsulates all uncertainty arising from parameter
variations and modeling errors. We now define the switch
-
ing function
Secece==+ +Ce
32211
, (16)
where
C =[]cc
12
10
and
c
i
(
i =12,
) are positive constants
to be chosen according to the desired dynamics of the
closed-loop system. The following control law is proposed [12]
uu u u
veqsw ft
=+ +,
(17)
where
u
bcav
c
M
aFa
eq
=−
=− + + +
+
−
−
()()
()
$
CB CA
1
1
1
112
2
13 14
Pr
c
M
F
u
uu
L
sw sw
ft fm t
1
2
−−
=−
=−
$
,
(),
(/
ρϕ
ϕγ
sgn
tanh ),
and where
rcxcxF
rrr
=++
12
&&&
&
,
ϕ= =SSbCB
1
, and
u
fm
and
γ
t
are some positive constants to be determined.
Higher Level Control
Task planning and execution are important considerations in
autonomous excavation. Traditionally, a planner is used to de
-
termine a sequence of primitive actions that, when executed,
will transform the world from an initial state to a goal state. As
there is a duality between world states and machine tasks,
planning is thought to be hierarchical. At the highest level, a
planner “ought to abstract the world and worry about goals
while lower levels incrementally flesh out the directives” [6].
For example, in excavation planning, the bucket trajectories
may initially be conservative, planned to shear a thin layer of
homogeneous soil during the “drag” phase of an excavation
step [22]. The objective is to generate trajectories that will fail
marginally in the absence of sensing and control. Another
view of planning is that it should be deliberative. A machine
employing deliberative reasoning requires relatively complete
knowledge of the world and uses this knowledge to predict
the outcome of its operations [23].
In our work on robotic excavation, we propose to com
-
bine behavior-based and hierarchical architectures to produce
strategies for planning and controlling the machine at higher
levels. Practically, excavation tasks can be decomposed into
behaviors that activate an appropriate set of suitable control
-
lers. Approximate reasoning with fuzzy logic is incorporated
to encapsulate human expert knowledge of earthmoving op
-
erations. The description of a particular behavior is based
mainly on observation and study of how expert operators
command excavators when digging.
Using a hierarchical-behavioral approach [24], we propose
a layered control hierarchy of lower and higher levels within a
global controller [22]. Lower level controllers are activated
upon the technical resolution of conflicts and also of resource
sharing, and the management of the control flow and the data
flow using statecharts [25] and fuzzy reasoning. At the higher
control level, the proposed hybrid architecture involves con
-
trol schemes that are based on the decomposition of typical
IEEE Robotics & Automation Magazine
24
MARCH 2002
This methodology can be extended
to coordinated control of
complicated autonomous machines
at many scales with a variety of
distinct dynamic operating
regimes.
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excavation tasks into states or state elements. Each task is rep
-
resented by a state in a statechart. At the lowest level in the
statechart hierarchy are state elements to be used for the con
-
trol of machine axes.
Unlike finite-state machines [26], statecharts allow for
component reuse, concurrency, and for complex nested states
(superstates), if required. A statechart is used to represent a
state machine consisting of states and transitions between states
together with synchronization states and pseudostates [25].
Every state object has entry and exit actions and executes the
particular behavior or activity until a transition is set and the
state is exited [22]. Transition conditions can be refined to
have event priorities if more than one condition is true at the
same time or can be seen as if/else or switch/case structures.
For transition between state elements, a characteristic function
γ
i
associated with the state
S
i
is defined here as follows
γ
i
ij
SS
i= , ,..
=
1
012
,
,
transition from to
is active,S
i
.,r.
(18)
A task element base,
{ , , ,.., }τ
i
ir=12
, can contain one or
more feasible elemental operations of the machine actuators
[22]: perhaps
τ
1
—adjust the engine throttle to maintain a con-
stant speed,
τ
2
—maintain the current position for a certain
time,
τ
3
—curl the bucket inward, and
τ
4
—curl the bucket
outward, and so on.
To illustrate this control architecture, consider the tasks of
loading and of digging a trench to a certain depth. In loading,
one pass in the task of removing soil from a pile can be decom-
posed as consisting of the states of positioning the bucket with
a specified attack angle, penetrating the soil by curling the
bucket inward, lifting and swinging the boom, and then
dumping soil by curling the bucket outward.
Trenching, another common construction task, can be de
-
composed to the following sequence of states within a statechart:
1) Position the bucket over the trench start.
2) Lower the boom to the ground surface.
3) Penetrate the ground by curling the bucket.
4) Drag the bucket teeth in a straight line by moving the
arm and the boom simultaneously.
5) Curl the bucket to collect soil into the bucket.
6) Raise the boom out of contact with ground.
7) Dump the bucket contents at the side of the trench.
8) Check the necessity of doing another dig cycle according
to the specified trench dimensions. If necessary, repeat steps
1-8, or else terminate the task.
Associated with each phase of the task decomposition de
-
scribed above are states
Si
i
, ,...,=15
, including LowerBoom
(
S
1
), Penetrate (
S
2
), Drag (
S
3
), Capture (
S
4
), and
LoadToTruck (
S
5
). The digging portion of the excavation
work cycle and the dump cycle are considered as sub-states of
this statechart. The transition between task elements is deter
-
mined mainly by estimating if the digger has reached a posi
-
tion predetermined according to expert excavator-operator
heuristics by measuring the Cartesian position error.
MARCH 2002
IEEE Robotics & Automation Magazine
25
Position [rad]
2.5
2
1.5
1
0.5
0
0
5
10 15 20 25 30 35 40 45 50
Time
(a)
Reference Input
Actual Position
Reference Input
Actual Position
Reference Input
Actual Position
Position [rad]
2.8
2.6
2.4
2.2
2
1.8
0
5
10 15 20 25 30 35 40 45 50
Time
(b)
Position [rad]
1.6
1.4
1.2
1
0.8
0.6
0
5
10 15 20 25 30 35 40 45 50
Time
(c)
1.6
1.4
0.4
0.2
Figure 5. Loading a truck with impedance control: (a) bucket, (b)
arm, and (c) boom joint angles.
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Experimental Results
Experiments have been performed using the robotic excavator
shown in Fig. 1. Data acquisition and control algorithms are
written in C++ and executed under the Windows NT operat
-
ing system. The sampling time is chosen to be 0.010 s, and
data is communicated between the five control system proces
-
sors by message-passing over a controller area network (CAN)
bus at 250 kb/s. Some typical excavation tasks in construction
automation can be demonstrated with our robotic machine.
Loading, for example, is a common earthmoving task. In
an experiment, the duration for one-pass loading is set at 50 s.
As the excavator operates under computer control, machine
data from joint encoders and pressure transducers, together
with environment data obtained by laser-scanning the soil
pile, are continually gathered. These data determine, in
real-time, the flow of control within the statechart, thus caus
-
ing the required digging action. Transition between task ele
-
ments (i.e., states) is determined mainly by testing estimated
joint positions against allowable position errors. Given specific
values of the target impedance matrices in (7), experimental
responses of the boom, dipper arm, and bucket joints to refer
-
ence position inputs are as shown in Fig. 5. Fig. 6 shows the
ram force that actuates the bucket for one loading cycle of dig-
ging and dumping the soil. The results obtained verify the va-
lidity and feasibility of our proposed robust control scheme for
autonomous operation of the robotic excavator, taking into
account tool-soil interactions.
Fig. 1 shows one phase of a trenching task, executed with the
decomposition detailed in the “Higher-Level Control” section.
The average task duration for the digging portion of the trench-
ing cycle is 15 s, which is an average time for a human operator to
complete this task. The recorded data for the entry and exit
points of the dragging phase are utilized to generate the desired
trajectory in joint space for the next digging cycle. Field tests
have been conducted that involve trenching in soils categorized
as soft, medium, and hard. The three parts of Fig. 7 show the
measured Cartesian trajectories of the bucket tip when digging in
these three soil types. In the figure, segments AB, BC, CD, and
IEEE Robotics & Automation Magazine
26
MARCH 2002
Force [N]
8000
7000
6000
5000
4000
3000
2000
1000
0
−1000
−2000
0
5
10 15 20 25 30 35 40 45 50
Time [s]
Figure 6. Bucket ram force during a loading task.
−0.2
−0.3
−0.4
−0.5
−0.6
−0.7
−0.8
−0.9
−1
−1.1
0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4
Digging Trajectory
X (m)
Z (m)
(a)
E
s4
D
s3
C
s2
B
s1
A
Z (m)
−0.1
−0.2
−0.3
−0.4
−0.5
−0.6
−0.7
−0.8
−0.9
−1
0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4
Digging Trajectory
X (m)
Z (m)
(b)
E
s4
D
s3
C
s2
B
s1
A
−0.2
−0.4
−0.6
−0.8
−1
−1.2
0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4
Digging Trajectory
X (m)
Z (m)
(c)
E
s4
D
s3
C
s2
B
s1
A
−1.1
Figure 7. Bucket tip trajectory, digging (a) soft, (b) medium, and (c)
hard soil.
Authorized licensed use limited to: University of Technology Sydney. Downloaded on December 17, 2008 at 00:11 from IEEE Xplore. Restrictions apply.
DE respectively correspond to the previously defined phases
S
1
,
S
2
,
S
3
, and
S
4
. In the absence of hard inclusions, the tip motion
during the dragging phase is observed in Fig. 7(a) and (b) to satisfy
a desired tolerance of 5 cm. Fig. 7(c) shows that the bucket teeth
cannot, however, penetrate hard soil smoothly because the re
-
quired soil cutting force exceeds the excavator’s force capacity.
The bucket must then be lifted and the surface scratched with the
bucket teeth in order to loosen the soil underneath.
Conclusion
An overview of the robotic excavation project at the Austra
-
lian Centre for Field Robotics at The University of Sydney
has been presented. The experimental machine, retrofitted
from a commercial mini-excavator, and its instrumentation
are described. Estimation and control strategies applied to the
robotic excavator digger are briefly presented. Variable-struc
-
ture-based techniques are employed to implement impedance
control of excavator dynamics, and position/force control of
the electrohydraulic systems for each working axis. This con
-
trol takes into account uncertainties in modeling, friction, and
bucket-soil interactions.
Behavioral and hierarchical approaches are combined for
decomposition and execution of some excavation tasks that
are common in construction. The control architecture is de-
signed with a view to managing hierarchical complexity and
facilitating the application of formal verification methods,
software reuse, and lower-level robust control results. It is be-
lieved that the methodology can be extended to coordinated
control of complicated autonomous machines at many scales
with a variety of distinct dynamic operating regimes. The ex-
perimental results described here suggest the technical possi-
bility of achieving autonomous robotic excavation in moving
toward construction automation.
Acknowledgments
The Australian Centre for Field Robotics is a Commonwealth
Key Centre of Teaching and Research. Support from the
Australian Research Council, NS Komatsu Pty. Ltd., and
Cooperative Research Centre for Mining Technology and
Equipment is gratefully acknowledged.
Keywords
Robotic excavation, construction automation, lower and
higher level control, impedance control, statecharts.
References
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Quang Ha received a B.E. in electrical engineering from
Ho Chi Minh City University of Technology, Vietnam, in
1983; a Ph.D. in engineering science from Moscow Power
Institute, Russia, in 1992; and a Ph.D. in electrical engineer
-
ing from the University of Tasmania, Australia, in 1997.
From 1997-2000, he was a senior research associate at the
Centre for Field Robotics at The University of Sydney. He is
currently a lecturer at the University of Technology, Syd
-
ney, Australia. His research interests include nonlinear con
-
trol, variable structure systems, robotics, and applications of
artificial intelligence in engineering.
Miguel Santos received a B.S. in electrical engineering from
the Universidad Nacional de Ingenieria, Peru, in 1988 and a
M.S. in engineering science from the University of New
South Wales, Australia, in 1995. His research interests include
robot architectures, fuzzy control, artificial intelligence, and
its applications to construction and mining.
Quang Nguyen received a B.S. In electrical engineering
from Hanoi University of Technology, Vietnam, in 1993. He
recently graduated with a PH.D. from the Australian Centre
for Field Robotics, The University of Sydney, Australia. His
research interests include nonlinear control, robotics and soft-
ware engineering.
David Rye received a B.E. from Adelaide University, Aus-
tralia, and a Ph.D. from The University of Sydney, Australia,
both in mechanical engineering, in 1980 and 1986, respec
-
tively. From 1986 to December 1987, he served as a lecturer
in mechanical engineering at the Department of Mechanical
Engineering at the University of Newcastle, Australia. Since
1988, he has been with the Department of Mechanical and
Mechatronic Engineering, The University of Sydney, Aus
-
tralia, as a lecturer and then senior lecturer in mechanical en
-
gineering. Dr. Rye is a Deputy Director of the Australian
Centre for Field Robotics. His research interests include in
-
telligent and nonlinear control, autonomous excavation,
crane dynamics and control, mechatronics and automation.
Hugh Durrant-Whyte received the B.S. in mechanical and
nuclear engineering from the University of London, UK, in
1983, and an M.S.E. and Ph.D. in systems engineering from
the University of Pennsylvania, USA, in 1985 and 1986, re
-
spectively. From 1987-1995, he was a University Lecturer in
Engineering Science, Oxford University, UK. Since July
1995, he has been Professor of Mechatronic Engineering at
the Department of Mechanical and Mechatronic Engineering,
The University of Sydney, Australia. Prof. Durrant-Whyte is
the Director of the Australian Centre for Field Robotics. His
research interests include sensor data fusion, sensor systems,
and mobile robotics.
Address for Correspondence: Dr. David Rye, Australian Cen-
tre for Field Robotics, Rose Street Building J04, The Univer-
sity of Sydney 2006, Australia. Phone: +61-2-9351-7126;
Fax: +61-2-9351-7474; E-mail: rye@acfr.usyd.edu.au; Web
site: http://www.acfr.usyd.edu.au/.
MARCH 2002
IEEE Robotics & Automation Magazine
28
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