A Friendly Beast of Burden A Human Assistive Robot for Handling Large Payloads
A Friendly
Beast of Burden
A Human-Assistive Robot for Handling Large Payloads
© ISTOCKPHOTO.COM/MIHAJLO MARICIC
By Clément Gosselin, Thierry Laliberté, Boris Mayer-St-Onge,
Simon Foucault, Alexandre Lecours, Vincent Duchaine,
Noémie Paradis, Dalong Gao, and Roland Menassa
T
his article presents a novel robotic assistive device
for the handling of large payloads. The design of the
robot is based on the application of the following
fundamental mechanical principles: inertia is minimized, a parallel closed-loop cable/belt routing system is used to kinematically decouple the transmission from
fixed actuators and to the end-effector, and variable static balancing is used to minimize the actuation forces required for
vertical motion. As a result, the device requires only low
power, thereby improving safety, and can be operated manually, even in the event of a power failure (with minimum
backup power for brake release). A novel force/torque sensor is
also introduced along with a control algorithm based on variable admittance that provides a very intuitive interface for
Digital Object Identifier 10.1109/MRA.2013.2283651
Date of publication: 30 October 2013
1070-9932/13/$31.00©2013IEEE
physical human-robot cooperation. Finally, a full-scale prototype integrating all of the above concepts is presented.
Designing a Safe Robotic Device
Human-robot cooperation is seen by many as one of the key
challenges of 21st century robotics. To expand their applications and the variety of services to humans, robots must
somehow be integrated into human activities.
Over the last decade, several research initiatives have focused
on the development of safe and intuitive robots for humanrobot cooperation [1]–[3]. Several principles have been proposed to design intrinsically safe robots, such as the introduction
of compliance in their actuated joints. The safety issue is even
more critical when large and/or heavy payloads are to be handled. The latter situation is common in industrial tasks such as
in automotive assembly lines, where some form of mechanical
assistance is required to help human operators manipulate heavy
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payloads and avoid ergonomic stress. Robotic devices have been
proposed in the past to perform such tasks, e.g. [4]. Although
existing robotic devices can be used, provided that safety standards are followed [5], it is desirable to improve the safety and
intuitivity of present-day robotic assistants.
The current research work aims to develop an intrinsically
safe robotic device capable of handling large payloads in an
industrial setting. To this end, fundamental issues are
addressed up front and a robotic prototype is introduced that
includes several novel principles. The degrees of freedom
(DoF) allowed by the robotic system presented in this article
are the so-called group of Schonflies motions [also referred to
as the Selective Compliance Assembly Robot Arm (SCARA)type motions], i.e., displacements in all three directions ^ XYZ h
and rotations i around one direction. Here, rotations are
assumed to take place about a vertical axis. SCARA-type
motions are very common in assembly operations, thereby
justifying their choice for the development of a prototype of a
human augmentation robot.
The following issues are addressed in this article. First, the
mechanical architecture is considered and a C-shaped endeffector structure is proposed to minimize the internal forces
Runway Rails
Bridge Rails
Figure 1. A computer-aided design (CAD) model of the prototype
of the intelligent assist device.
Static Balancing Pulley
Bearing
Emergency Brake
Linear Guide
and Carriages
Cockpit Load Tool
Control
Handle
Figure 2. A prototype of a C-shaped end effector.
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and power. Kinematically decoupled transmission schemes
with fixed actuators are also proposed, and static balancing is
introduced as a means of drastically reducing the power of
the robotic device. A novel force/torque sensor based on
optical components is then proposed to significantly reduce
the force signal noise and eliminate the drift, thereby
improving control performance and intuitivity. Finally a control algorithm based on variable admittance is introduced,
and a prototype is presented.
Motion Range and Effectiveness:
Mechanical Architecture
Minimizing the input power of a robotic device is one of the
most effective means of improving safety. In the context of
large payloads being handled by operators using assistive
devices, accelerations are rather limited and, hence, the main
external force involved in the handling of the payload is the
support of its weight. Therefore, the weight support should
be decoupled from the other actuation DoF and handled
separately. This can be accomplished using the conventional
bridge-and-trolley arrangement depicted in Figure 1, which
was developed in this work. Moreover, the bridge-and-trolley
architecture provides an effective way of producing large displacements while introducing limited additional inertia. It
also provides a well-defined and well-shaped workspace.
In this architecture, a mast is moved in the horizontal
direction (and rotated around a vertical axis). The up/down
motion of the mast is decoupled from the other DoF, the latter requiring only limited power. Moreover, to limit the internal forces in the bridge-and-trolley system, the center of mass
of the payload should be roughly aligned with the supporting
mast. To this end, a C-shaped end-effector is used here
instead of a conventional L-shaped end-effector. In the
C-shaped end-effector, the payload undergoes rotations
around a vertical axis passing through its center of mass,
which minimizes the rotational inertia (and thus the power of
the actuator associated with this rotation). Additionally,
because the payload’s weight is applied at the center of the
trolley, the moments applied on the bridge are also minimized, which reduces the bending loads. The mass of the trolley components can therefore be reduced. The C-shaped endeffector is shown in Figure 2. The design also includes a
variation of Elisha Otis’ elevator safety brake patented in 1861.
In case of failure of the vertical supporting system, the endeffector is prevented from dropping.
Mechanical Innovation: Closed-Loop
Cable/Belt Transmissions
In conventional bridge-and-trolley systems, actuators are
mounted on moving components (bridge or trolley) and
motion is produced with friction wheels rolling on the runway
rails and on the bridge rails, as seen in [4]. Although this
approach is conceptually simple, it requires that the actuators
be mounted on moving links, including a powerful actuator
needed for the vertical motion of the payload, which increases
the moving mass and inertia.
A different approach is used here. The actuators are fixed
to the ground structure, and cable/belt routings are used to
drive the motion of the end-effector. Open-loop routings
require that the cables be wound on winches and that at least
one more actuator than DoF be used, leading to more complex control and winding issues. Closed-loop routings are
therefore proposed here as a simpler and more effective alternative. As explained in [6], a system with closed-loop routings
requires only as many actuators as DoF. Also, the initial tension in the closed cable/belt loops is independent from the
control system and is established at the mounting by a
mechanical tightening system. Closed-loop routings also have
an advantage of eliminating the need for spools since the total
length of each loop is constant, thereby alleviating the winding issues. As a result, components other than cables, such as
timing belts, can be used. Furthermore, as explained in [6],
maintaining the transmission system fully decoupled provides
several advantages, namely, minimization of the actuation
power and minimization of the maximum end-effector
forces. Therefore, the routings proposed here are designed
accordingly. The transmission routings associated with each
of the DoF of the assistive robotic device are now detailed.
The routing corresponding to the vertical motion requires
special attention because this motion involves the weight of
the end-effector and payload. Therefore, vertical motion is
treated in a separate section.
Bridge
Attachment
Point
Cable or
Belt
Trolley
X
Displacement Along the Bridge Rails
The routing used for the displacement along the bridge rails
can be found in [7]–[10] and is illustrated schematically in
Figure 4(a). It is pointed out that this routing is independent
from the position of the bridge in the X direction. This is possible because of the two free pulleys on the Y displacement
trolley. The routing does not apply any torque on the bridge to
maintain its tension and when it is actuated.
Rotation Around a Vertical Axis
A cable routing allowing a fully decoupled rotation, i.e., a rotation decoupled from the X and Y motions, is proposed. This
routing is illustrated schematically in Figure 4(b). First, a translation is produced on the end-effector, which is then converted
into a rotation by a rack and pinion or an equivalent mechanism. This routing does not induce any torque on the bridge to
maintain its tension, but it produces a torque when actuated.
Indeed, different tensions occur along the cable/belt when it is
actuated. Then, because of the routing configuration, the different reaction forces applied on the pulleys attached to the
Free Pulley
Y
End Effector
Figure 3. The routing used for the displacement along the
runway rails.
i2
i4
(a)
Displacement Along the Runway Rails
A fixed reference frame is defined with its X axis along the
direction of the runway rails, its Y axis along the direction of
the bridge rails, and its Z axis vertical. To avoid generating a
residual torque on the bridge, the routing illustrated in
Figure 3 and found in [7] and [8] is used here to produce
motion along the runway rails. The cable/belt loop drives the
bridge via two points, which provides stability.
Actuated
Pulley
i1
Workspace
(b)
Figure 4. The routing used for the (a) displacement along the
bridge rails and (b) rotation around a vertical axis.
bridge result in a torque. This is an additional incentive to
minimize the inertia through the use of a C-shaped end-effector, as proposed earlier.
Alleviating Gravity: Vertical Actuation
and Variable Static Balancing
Vertical Actuation
The transmission routing for the displacement along the vertical direction is different from that of the other axes since it is
the only axis that involves gravity. Assuming accelerations considerably smaller than the gravitational acceleration, the forces
and torques involved in this routing are significantly larger
than in the other routings. Therefore, the transmission routing
for the vertical motion should preserve the decoupling
between the vertical motion and the other motions to separate
routings with different magnitudes of forces. This routing is
illustrated schematically in Figure 5 and is similar to the concept found in [10]. However, it is much more stable because it
avoids applying a large torque on the bridge and rails system. It
is easily observed from Figure 5 that horizontal motions have
no effect on the loop driving the vertical motion and vice
versa. Therefore, the vertical motion is completely and always
decoupled from the horizontal motions. Since it is not a planar,
the loop associated with the vertical motion is preferably built
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using a cable. Actuation can be provided by a pulley fixed to
the ground that is feeding the cable (e.g., the pulley identified
as i 3 in Figure 5). However, in the prototype developed in this
work, the vertical motion is driven by a translation of the pulley that supports the counterweight with the help of a linear
slider that includes a ball screw.
Static Balancing
Since the actuation of the vertical motion is completely
decoupled from the other motions, it is the only DoF that
requires large input forces. However, the large input forces are
due to gravity, a predictable and consistent force that can be
compensated by using static balancing. Static balancing has
the advantage of drastically reducing the actuation forces and
therefore significantly improving the safety of the robot. To
this end, a counterweight has been included in the cable routing, as illustrated in Figure 5.
Because of the decoupling feature of the routing proposed
for the Z displacement, the counterweight only has to move
along the vertical direction and does not add inertia along the
other axes of motion. It is noted that a similar feature can be
found in [11] but for the decoupling between the vertical
motion and only one other axis of motion.
Z
Y
X
Figure 5. The balancing routing that does not apply a torque on
the bridge.
dL
dC
O
F
MC g
Figure 6. The principle of the balancing system.
•
Fd L = M L gd L = M C gd C, d C = M L d L ,
MC
(1)
where F is the balancing force (which corresponds to the
mass of the load to be balanced, M L), M C is the mass of the
counterweight, g is the gravitational acceleration, d L is the
distance between the point of application of F and the fixed
pivot O, and d C is the variable distance between the center of
mass of the counterweight M C and the fixed pivot O.
In the balancing system, the mass of the counterweight M C
and the location of the force d L are fixed. Therefore, a variation in M L can be compensated for by a change in d C, according to (1). Finally, the vertical actuation is included in the static
balancing routing. An actuator and a ball screw are used to
perform the vertical motion. However, as mentioned above,
the forces required at the actuator are only a fraction of the
weight of the end-effector and payload because the static
weight is always compensated for by the counterweight.
Communicating the User’s Intentions: Force/
Torque Sensor Based on Photointerruptors
i3
142
Variable Balancing Principle
The payload to be handled by the robotic device can be varied.
For instance, the device can be loaded or unloaded. Therefore,
the counterweight should be actively adjusted. To this end, the
counterweight is mounted on a lever and an actuator is used to
displace the counterweight along the lever. This lever is
designed so that its angular motion is relatively small.
Therefore, referring to Figure 6, static equilibrium requires that
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Context
The robotic system presented in this article aims at providing
assistance to a human operator in a variety of ways.
In the interactive mode, the operator makes use of a sensing
handle to apply forces to the device. The input forces are used to
infer the intentions of the operator and provide a simultaneous
intuitive control of the four DoF. Therefore, it is important that
the sensing handle is able to provide a well-conditioned force/
torque signal with low noise and no significant drift.
As pointed out in [12], force/torque sensors based on
strain gauges are sensitive to noise and drift and are not wellsuited for applications in haptics. Instead, optical components can be used in combination with compliant mechanisms. In this work, a 4-DoF force/torque sensor based on
photointerrupters (PIs) was developed by combining singleaxis compliant mechanisms.
Single-Axis Force Sensor
Each single-axis sensor, illustrated schematically in Figure 7,
uses a compliant mechanism to convert the applied force into a
linear displacement. This displacement is then measured
through the gradual interruption of the light beam of a PI,
which is an optical sensor composed of an infrared emitter and
a phototransistor placed face to face. PIs have many advantages
such as noncontact displacement measurement, very low noise
sensitivity, minimal drift, and low cost. The compliant
mechanism is a parallelogram composed of stainless steel spring
leafs built using an electrical-discharge machine. For redundancy, two PIs are attached to the compliant mechanism. The
compliant mechanism has the advantage of being very compliant along the measured direction compared to the compliance
along the other directions, which leads to a decoupled behavior
of the 4-DoF assembly. To protect the flexures from any potential overload along the sensor axis, inner mechanical stops are
included. Also, to finely adjust the position of the PIs, the latter
are installed on a mobile bar. This mobile part is installed on
compliant rings and pulled by screws, allowing for fine adjustment of the position using two adjustment screws.
Sensor Assembly
To measure the applied forces, the sensing handle combines a
minimal number of four single-axis force sensors, mounted as
shown in Figure 8. The mechanical assembly is composed of a
pair of sensors mounted in parallel (A) combined with two
other sensors mounted in series (B and C), as illustrated on the
simplified schematic of Figure 8, in which the compliant axis
of each sensor is represented by a prismatic joint. While the
single-axis sensors (B) and (C) each measure one force acting
along their respective axis ( Fx and Fz and respectively), the
pair of sensors mounted in parallel (A) acts as a bar pivoting
about its center, allowing a torque measurement as well as a
force measurement. This serial assembly of three components
forms the link between the manipulated part of the handle and
its fixed part. In other words, the handle is attached to the pair
of sensors (A) by two rotational joints, while the last single-axis
sensor (C) is rigidly attached to the base of the handle.
Sensing Handle
To provide safe and intuitive control of the assistive device,
the handle is mounted on the exterior vertical beam of the
C-shaped effector, at a safe distance from the payload. The
handle dimensions have been chosen to satisfy the ergonomic
standards specified in [13]. Finally, two pairs of through beam
photoelectric sensors are added to the handle to detect the
presence of the operator’s hands on each side of the handle, as
prescribed by safety norms for hands-on control mode [5].
The sensing handle is shown in Figure 2, where it is referred
to as the control handle.
For comparison purposes, an example of the force signal
obtained from the sensing handle proposed here is plotted in
Figure 9 together with a signal obtained from a commercial
six-axis force/torque sensor based on strain gauges. It can be
observed that the noise is considerably lower with the force/
torque sensor based on PIs. It was also verified experimentally that the drift is significantly reduced with the sensing
handle based on PIs, when compared with the sensor based
on strain gauges.
autonomous mode, the robot moves by itself, for instance, to
fetch a part and present it to the operator. In interactive mode,
the operator guides the motion of the robot using the sensing
handle. Finally, in passive mode, the robot is powered off and
the operator is moving the end-effector. This mode can be
used in case of a failure of the controller or actuators.
In autonomous mode, the robot is controlled using a conventional proportional integral derivative controller, which is
not discussed in this article. In interactive mode, a control
scheme based on admittance control is implemented.
Admittance control accepts a force as a measured input and
produces a displacement (see [14] and [15]). This is in contrast with impedance control, which accepts a displacement as
a measured input and produces a force. Impedance control
Spring Leafs
Mobile Bar
Mechanical Stop
Adjustment
Screw
PI
Figure 7. A schematic representation of one of the single-axis
force sensors.
r
A
C
B
Z
Y
C
X
A
B
Intuitive Interaction: Inference
and Control Algorithms
Three motion modes are considered here, namely autonomous mode, interactive mode, and passive mode. In
Figure 8. A CAD model and schematic representation of the
assembled housings of the sensing handle.
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Because the manipulation of large and heavy payloads is
considered here, large inertia and friction forces are expected,
making an impedance controller inappropriate. On the other
hand, an admittance controller is well suited for such applications and, hence, the latter is used here. The one-dimensional
admittance equation is written as [14]
30
20
10
Force (N)
0
fH = m (xp - xp 0) + c (xo - xo 0) + k (x - x 0),
(2)
-10
-20
-30
-40
-50
0
Commercial Sensor
PI Sensor
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Time (s)
Figure 9. The force signal obtained with the proposed sensor and
a commercial force/torque sensor.
where fH is the interaction force, i.e., the force applied by the
human operator, m is the virtual mass, c is the virtual damping, k is the virtual stiffness, x 0 is the equilibrium point, and
x, xo , and xp are respectively the position, velocity and acceleration. The virtual parameters (mass, damping, and stiffness)
correspond to the prescribed behavior perceived by the user.
Since it is desired to simulate free motion, the stiffness, k, as
well as the desired position, x 0, the desired velocity, xo 0 , and
the desired acceleration, xp 0 , are set to zero. The admittance
equation is then rewritten as
fH = mxp + cxo .
The trajectory to be followed by the robot can be prescribed
as a desired position, x d, or as a desired velocity, xo d . For
velocity control, the desired velocity can be written, in the
Laplace domain, as
Velocity
Human
Force
Xo d (s) =
Robot
Force Sensor
Measured Force
Controller
vd
Admittance
Model
vd
(3)
Saturation
and Limits
Velocity
Controller
Command
Measured
Velocity
FH (s)
FH (s) /c
=
= FH (s) H (s),
ms + c
m s +1
c
(4)
where Xo d (s) is the Laplace transform of xo d, FH (s) is the
Laplace transform of fH , and s is the Laplace variable.
Velocity control is used here, similarly to what was presented
in [16]–[18]. Indeed, with position control, the robot would
be attracted to a given reference position, which does not represent the desired behavior. A discretized desired velocity is
obtained with a zero-order-hold (a bilinear discretization
could also be used) and is represented by
xo d (k) = ;
fH (k) - cxo d (k - 1)
E Ts + xo d (k - 1),
m
(5)
where fH (k) is the interaction force at time step k, xo d (k) is
the desired velocity, and Ts is the sampling period. The
desired acceleration at time step k, noted xp d (k), is then represented by
Mechanism
xp d (k) =
fH (k) - cxo d (k - 1)
.
m
(6)
Figure 10. The control scheme using a velocity controller.
represents the vast majority of applications in haptics literature, while admittance control is less commonly used.
However, impedance control is most appropriate for devices
with low inertia and friction since the user will inevitably feel
these forces.
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Figure 10 presents the control scheme used in this work
where a proportional controller is used as the velocity controller. No derivative gain is used to improve robustness (the
acceleration signal is typically noisy), and no integral gain is
used because the behavior to an operator input would then
depend on the error history and would be counterintuitive.
The transfer function between the input force and the output velocity corresponds to that of a first-order system, given
in (4), where the parameters are the virtual mass ^mh and the
virtual damping ^ c h . One has
fH
Admittance
H (s) = 1/c .
m s +1
c
(7)
c v = c f - a a exp d e
c v = c f + a d exp d e
for acceleration
for deceleration
Desired Motion
x˙d
With the transfer function H (s), it becomes apparent that the
virtual damping affects the steady state value of the response
while the ratio of the virtual mass over the virtual damping
affects the dynamics (it changes the pole of the first-order system). It is recognized in the literature that the damping parameter has a greater influence on human perception than the virtual mass [16]. When the admittance parameters (virtual mass
and virtual damping) are set to high values, a larger force is
generally required to move the robot at a given velocity and/or
acceleration. However, it is easier to perform fine movements
since the robot is less reactive and interaction is thus smoother.
When the parameters are set to low values, it is then generally
easy to move the robot at high velocity and/or acceleration but
more difficult to perform fine movements. In summary, there
is a compromise between the force required to move the robot
and the ability to perform fine movements. This is the main
drawback of a fixed admittance control. Therefore, in this
work, a variable admittance control approach is used. The
objective of variable admittance control is to adjust the admittance parameters according to the inferred human intentions.
In other words, high admittance parameters are desired when
the operator performs fine movements, while lower values of
the parameters should be used when movements involving
large accelerations are performed.
The approach used here is presented in more detail in [19].
First, the intentions of the operator are inferred by computing
the desired acceleration using (6) and by applying the scheme
shown in Figure 11. Admittance parameters are then adjusted
accordingly, as explained in the following.
When acceleration is desired, both virtual damping and
virtual mass should be decreased. Although several solutions
are possible, here the virtual mass to virtual damping ratio is
kept constant, i.e., maintained at the default values. The virtual mass to virtual damping ratio being constant, the dynamics of the device will remain similar [see (7)], which is more
intuitive for the operator.
When a deceleration is desired, ideally, the damping
should be increased while the mass is decreased. To produce
this behavior while maintaining continuity of the parameters,
an exponential function is used to compute the virtual mass.
With this approach, the designer can choose a minimum virtual mass to virtual damping ratio and set a transition
smoothness parameter.
Equations for the variable damping and variable mass are
heuristically chosen as
(8)
x¨d
Sign
Sign
=?
Yes
Accelerate
at x¨d
No
Inferred Human Intention
Decelerate
at x¨d
Figure 11. The scheme used to infer human intentions.
Figure 12. The end-effector of the prototype of the 4-DoF robotic
assistive device.
and
m f cv
for acceleration
cf
mf
^ 1 - b ^ 1 - e -c^c v -c f hhh c v for deceleration , (9)
mv =
cf
mv =
where m v is the effective virtual mass, c v is the effective virtual damping, m f is the default virtual mass, c f is the default
virtual damping, b ^0 < b < 1h is a parameter used to adjust
the steady state virtual mass to virtual damping ratio, c is a
parameter used to adjust the smoothness with which the
ratio changes, and a a and a d are parameters to be tuned. In
practice, for a given maximum magnitude of xp d, denoted
exp d e max, a rough estimate of a a and a d can be obtained by
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which uses a cable. All closed loops are driven using brushless
servo actuators mounted on the fixed frame. Most mechanical
components are composed of steel.
The fourth DoF (vertical motion) uses the routing presented
in Figure 5 for static balancing. A steel cable is used for the vertical motion because of the large forces involved. The balancing
is implemented using a pivot and lever with a counterweight
that can be moved along the lever, according to the principle
explained in Figure 6. The balancing system can be seen on the
right-hand side of the prototype in Figures 1 and 13.
Figure 13. A prototype of the 4-DoF robotic assistive device.
(Photo courtesy of the Laval University Robotics Laboratory.)
Figure 14. The execution of the drawing task with the prototype.
(Photo courtesy of the Laval University Robotics Laboratory.)
preventing c v from reaching the minimum, c min, or maximum, c max, allowed damping
c f - c min
exp d e max
c max - c f
.
ad .
exp d e max
aa
.
(10)
Performance Demonstration: Prototype
Development and Experiments
A prototype of the 4-DoF robotic assistive device proposed in
this article was built at Laval University as part of a collaborative research initiative between the Laval University Robotics
Laboratory and the General Motors Global Research Centre.
The prototype includes all the innovations presented in the
previous sections, namely the closed-loop transmission routings, the variable static balancing principle, the sensing handle
based on PIs, and the novel control algorithms. It was developed in the context of assembly line assistance for the automotive industry. Photos of the prototype are shown in Figures 12
and 13. The prototype has a payload capability of 113 kg, a
workspace of 3.3 (L) # 2.15 (W) # 0.52 (H) m, and a rotational range of motion of 120˚ about the vertical axis.
Mechanical Implementation
The closed-loop mechanical transmissions are implemented
using timing belts for all DoF except for the vertical motion,
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Controller Hardware
The controller of the assistive robotic device was built following
a proven configuration used in industrial robotics. In this configuration, a personal computer (PC) is used as a central unit
while a programmable logic controller (PLC) is used to monitor all components (including the PC) and deal with safety/reliability. One of the advantages of this configuration is to separate
the software part of the controller from the hardware.
The hardware is composed of off-the-shelf components,
namely a dual-core 3-GHz PC, a Sensoray 626 acquisition
card, and an Allen-Bradley MicroLogix 1769 PLC. The actuators are controlled using Copley Xenus XTL-230-40 drives.
Path planning algorithms and torque calculations are executed by the PC.
Simulink and RT-Lab are used to program the control
algorithms. The PC/PLC combination together with RT-Lab
leads to a stable, robust and yet flexible controller, running at
a servo rate of 500 Hz.
Performance
Videos demonstrating the features and the effectiveness of the
prototype can be found at: http://robot.gmc.ulaval.ca/en/
research/theme507.html. Photos taken during the experiments are also shown in Figures 14 and 15. Among other
experiments, a drawing task was used to test the dexterity and
fine motion control of the prototype (see Figure 14). The
drawing task consisted of starting from a given point, advancing for 1.5 m, avoiding an obstacle by turning 90˚, advancing
1.25 m, and then moving to the drawing board by performing
a 1-m lateral displacement. Finally, the operator had to follow
a maze while tracing the path on a piece of paper with a pen
mounted on the robot at 1.4 m from the handle. The performances obtained with this task [19] are a clear demonstration
of the control capabilities.
Other experiments involved the lifting and manipulation of
heavy payloads of up to 100 kg. Barbells were used to emulate
the payloads, as shown in Figure 15. During such typical cooperation tasks, the average power used by the robot is approximately 390 W. This low power resulted due to static balancing.
In addition to providing safety, static balancing allows the payload to be handled manually in all DoF even in the occurrence
of a failure (passive mode of motion). In the latter mode, demonstrated in the videos, the brakes are released and the mechanism is moved manually. Although not ergonomically sustainable, this mode of operation is very important because it
Figure 15. The prototype handling a heavy payload. (Photo
courtesy of the Laval University Robotics Laboratory.)
provides the ability to manually remove the robot from a given
continuous operation (an assembly line) in case of failure.
Conclusion
This article presents the design and control of a novel 4-DoF
assistive robot for the handling of large payloads. In this
design, the actuators are fixed and a set of decoupled cable/belt
closed-loop routings are used to produce the motion of the
end-effector. Additionally, static balancing is used to drastically
reduce actuator power and torque. A novel force/torque sensor
is also integrated in the robot, and control algorithms based on
variable admittance are used. A full-scale prototype integrating
all of the above concepts was built and tested. The low-inertia
and low-power design combined with drift-free and low-noise
force/torque measurements as well as effective control algorithms lead to a very intuitive physical human-robot interaction, as shown in the supporting videos.
Acknowledgments
The authors would like to acknowledge the financial support
of the Natural Sciences and Engineering Research Council of
Canada (NSERC, Grant RDCPJ 334898) and of General
Motors of Canada.
References
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425 464, June 20, 1995.
[12] S. Hirose and K. Yoneda, “Development of optical six-axial force sensor
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[13] “Ergonomics engineering/vendor guidelines for assists and specialized
tools,” GMVO Ergono., p. 1–3, June 2005.
[14] L. Villani and J. De Schutter, “Force control,” in Handbook of Robotics, B.
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[15] R. van der Linde and P. Lammertse, “HapticMaster—A generic force controlled
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[16] V. Duchaine and C. Gosselin, “General model of human-robot cooperation using a novel velocity based variable impedance control,” in Proc. World
Haptics, Mar. 2007, pp. 446–451.
[17] T. Tsumugiwa, R. Yokogawa, and K. Hara, “Variable impedance control based
on estimation of human arm stiffness for human-robot cooperative calligraphic
task,” in Proc. IEEE Int. Conf. Robotics Automation, 2002, vol. 1, pp. 644–650.
[18] R. Ikeura and H. Inooka, “Variable impedance control of a robot for cooperation with a human,” in Proc. IEEE Int. Conf. Robotics Automation, 1995,
vol. 3, pp. 3097–3102.
[19] A. Lecours, B. Mayer-St-Onge, and C. Gosselin, “Variable admittance
control of a four-degree-of-freedom intelligent assist device,” in Proc. IEEE
Int. Conf. Robotics Automation, May 14–18 2012, pp. 3903–3908.
Clément Gosselin, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: gosselin@gmc.
ulaval.ca.
Thierry Laliberté, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: thierry@gmc.
ulaval.ca.
Boris Mayer-St-Onge, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: boris@gmc.ulaval.ca.
Simon Foucault, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: foucault@gmc.
ulaval.ca.
Alexandre Lecours, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: alexandre.
lecours.1@ulaval.ca.
Vincent Duchaine, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: vincent.duchaine@
etsmtl.ca.
Noémie Paradis, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: noemie.paradis.1@
ulaval.ca.
Dalong Gao, GM Global Research, Warren, Michigan, USA.
E-mail: dalong.gao@gm.com.
Roland Menassa, GM Global Research, Warren, Michigan,
USA. E-mail: roland.menassa@gm.com.
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Beast of Burden
A Human-Assistive Robot for Handling Large Payloads
© ISTOCKPHOTO.COM/MIHAJLO MARICIC
By Clément Gosselin, Thierry Laliberté, Boris Mayer-St-Onge,
Simon Foucault, Alexandre Lecours, Vincent Duchaine,
Noémie Paradis, Dalong Gao, and Roland Menassa
T
his article presents a novel robotic assistive device
for the handling of large payloads. The design of the
robot is based on the application of the following
fundamental mechanical principles: inertia is minimized, a parallel closed-loop cable/belt routing system is used to kinematically decouple the transmission from
fixed actuators and to the end-effector, and variable static balancing is used to minimize the actuation forces required for
vertical motion. As a result, the device requires only low
power, thereby improving safety, and can be operated manually, even in the event of a power failure (with minimum
backup power for brake release). A novel force/torque sensor is
also introduced along with a control algorithm based on variable admittance that provides a very intuitive interface for
Digital Object Identifier 10.1109/MRA.2013.2283651
Date of publication: 30 October 2013
1070-9932/13/$31.00©2013IEEE
physical human-robot cooperation. Finally, a full-scale prototype integrating all of the above concepts is presented.
Designing a Safe Robotic Device
Human-robot cooperation is seen by many as one of the key
challenges of 21st century robotics. To expand their applications and the variety of services to humans, robots must
somehow be integrated into human activities.
Over the last decade, several research initiatives have focused
on the development of safe and intuitive robots for humanrobot cooperation [1]–[3]. Several principles have been proposed to design intrinsically safe robots, such as the introduction
of compliance in their actuated joints. The safety issue is even
more critical when large and/or heavy payloads are to be handled. The latter situation is common in industrial tasks such as
in automotive assembly lines, where some form of mechanical
assistance is required to help human operators manipulate heavy
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payloads and avoid ergonomic stress. Robotic devices have been
proposed in the past to perform such tasks, e.g. [4]. Although
existing robotic devices can be used, provided that safety standards are followed [5], it is desirable to improve the safety and
intuitivity of present-day robotic assistants.
The current research work aims to develop an intrinsically
safe robotic device capable of handling large payloads in an
industrial setting. To this end, fundamental issues are
addressed up front and a robotic prototype is introduced that
includes several novel principles. The degrees of freedom
(DoF) allowed by the robotic system presented in this article
are the so-called group of Schonflies motions [also referred to
as the Selective Compliance Assembly Robot Arm (SCARA)type motions], i.e., displacements in all three directions ^ XYZ h
and rotations i around one direction. Here, rotations are
assumed to take place about a vertical axis. SCARA-type
motions are very common in assembly operations, thereby
justifying their choice for the development of a prototype of a
human augmentation robot.
The following issues are addressed in this article. First, the
mechanical architecture is considered and a C-shaped endeffector structure is proposed to minimize the internal forces
Runway Rails
Bridge Rails
Figure 1. A computer-aided design (CAD) model of the prototype
of the intelligent assist device.
Static Balancing Pulley
Bearing
Emergency Brake
Linear Guide
and Carriages
Cockpit Load Tool
Control
Handle
Figure 2. A prototype of a C-shaped end effector.
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and power. Kinematically decoupled transmission schemes
with fixed actuators are also proposed, and static balancing is
introduced as a means of drastically reducing the power of
the robotic device. A novel force/torque sensor based on
optical components is then proposed to significantly reduce
the force signal noise and eliminate the drift, thereby
improving control performance and intuitivity. Finally a control algorithm based on variable admittance is introduced,
and a prototype is presented.
Motion Range and Effectiveness:
Mechanical Architecture
Minimizing the input power of a robotic device is one of the
most effective means of improving safety. In the context of
large payloads being handled by operators using assistive
devices, accelerations are rather limited and, hence, the main
external force involved in the handling of the payload is the
support of its weight. Therefore, the weight support should
be decoupled from the other actuation DoF and handled
separately. This can be accomplished using the conventional
bridge-and-trolley arrangement depicted in Figure 1, which
was developed in this work. Moreover, the bridge-and-trolley
architecture provides an effective way of producing large displacements while introducing limited additional inertia. It
also provides a well-defined and well-shaped workspace.
In this architecture, a mast is moved in the horizontal
direction (and rotated around a vertical axis). The up/down
motion of the mast is decoupled from the other DoF, the latter requiring only limited power. Moreover, to limit the internal forces in the bridge-and-trolley system, the center of mass
of the payload should be roughly aligned with the supporting
mast. To this end, a C-shaped end-effector is used here
instead of a conventional L-shaped end-effector. In the
C-shaped end-effector, the payload undergoes rotations
around a vertical axis passing through its center of mass,
which minimizes the rotational inertia (and thus the power of
the actuator associated with this rotation). Additionally,
because the payload’s weight is applied at the center of the
trolley, the moments applied on the bridge are also minimized, which reduces the bending loads. The mass of the trolley components can therefore be reduced. The C-shaped endeffector is shown in Figure 2. The design also includes a
variation of Elisha Otis’ elevator safety brake patented in 1861.
In case of failure of the vertical supporting system, the endeffector is prevented from dropping.
Mechanical Innovation: Closed-Loop
Cable/Belt Transmissions
In conventional bridge-and-trolley systems, actuators are
mounted on moving components (bridge or trolley) and
motion is produced with friction wheels rolling on the runway
rails and on the bridge rails, as seen in [4]. Although this
approach is conceptually simple, it requires that the actuators
be mounted on moving links, including a powerful actuator
needed for the vertical motion of the payload, which increases
the moving mass and inertia.
A different approach is used here. The actuators are fixed
to the ground structure, and cable/belt routings are used to
drive the motion of the end-effector. Open-loop routings
require that the cables be wound on winches and that at least
one more actuator than DoF be used, leading to more complex control and winding issues. Closed-loop routings are
therefore proposed here as a simpler and more effective alternative. As explained in [6], a system with closed-loop routings
requires only as many actuators as DoF. Also, the initial tension in the closed cable/belt loops is independent from the
control system and is established at the mounting by a
mechanical tightening system. Closed-loop routings also have
an advantage of eliminating the need for spools since the total
length of each loop is constant, thereby alleviating the winding issues. As a result, components other than cables, such as
timing belts, can be used. Furthermore, as explained in [6],
maintaining the transmission system fully decoupled provides
several advantages, namely, minimization of the actuation
power and minimization of the maximum end-effector
forces. Therefore, the routings proposed here are designed
accordingly. The transmission routings associated with each
of the DoF of the assistive robotic device are now detailed.
The routing corresponding to the vertical motion requires
special attention because this motion involves the weight of
the end-effector and payload. Therefore, vertical motion is
treated in a separate section.
Bridge
Attachment
Point
Cable or
Belt
Trolley
X
Displacement Along the Bridge Rails
The routing used for the displacement along the bridge rails
can be found in [7]–[10] and is illustrated schematically in
Figure 4(a). It is pointed out that this routing is independent
from the position of the bridge in the X direction. This is possible because of the two free pulleys on the Y displacement
trolley. The routing does not apply any torque on the bridge to
maintain its tension and when it is actuated.
Rotation Around a Vertical Axis
A cable routing allowing a fully decoupled rotation, i.e., a rotation decoupled from the X and Y motions, is proposed. This
routing is illustrated schematically in Figure 4(b). First, a translation is produced on the end-effector, which is then converted
into a rotation by a rack and pinion or an equivalent mechanism. This routing does not induce any torque on the bridge to
maintain its tension, but it produces a torque when actuated.
Indeed, different tensions occur along the cable/belt when it is
actuated. Then, because of the routing configuration, the different reaction forces applied on the pulleys attached to the
Free Pulley
Y
End Effector
Figure 3. The routing used for the displacement along the
runway rails.
i2
i4
(a)
Displacement Along the Runway Rails
A fixed reference frame is defined with its X axis along the
direction of the runway rails, its Y axis along the direction of
the bridge rails, and its Z axis vertical. To avoid generating a
residual torque on the bridge, the routing illustrated in
Figure 3 and found in [7] and [8] is used here to produce
motion along the runway rails. The cable/belt loop drives the
bridge via two points, which provides stability.
Actuated
Pulley
i1
Workspace
(b)
Figure 4. The routing used for the (a) displacement along the
bridge rails and (b) rotation around a vertical axis.
bridge result in a torque. This is an additional incentive to
minimize the inertia through the use of a C-shaped end-effector, as proposed earlier.
Alleviating Gravity: Vertical Actuation
and Variable Static Balancing
Vertical Actuation
The transmission routing for the displacement along the vertical direction is different from that of the other axes since it is
the only axis that involves gravity. Assuming accelerations considerably smaller than the gravitational acceleration, the forces
and torques involved in this routing are significantly larger
than in the other routings. Therefore, the transmission routing
for the vertical motion should preserve the decoupling
between the vertical motion and the other motions to separate
routings with different magnitudes of forces. This routing is
illustrated schematically in Figure 5 and is similar to the concept found in [10]. However, it is much more stable because it
avoids applying a large torque on the bridge and rails system. It
is easily observed from Figure 5 that horizontal motions have
no effect on the loop driving the vertical motion and vice
versa. Therefore, the vertical motion is completely and always
decoupled from the horizontal motions. Since it is not a planar,
the loop associated with the vertical motion is preferably built
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using a cable. Actuation can be provided by a pulley fixed to
the ground that is feeding the cable (e.g., the pulley identified
as i 3 in Figure 5). However, in the prototype developed in this
work, the vertical motion is driven by a translation of the pulley that supports the counterweight with the help of a linear
slider that includes a ball screw.
Static Balancing
Since the actuation of the vertical motion is completely
decoupled from the other motions, it is the only DoF that
requires large input forces. However, the large input forces are
due to gravity, a predictable and consistent force that can be
compensated by using static balancing. Static balancing has
the advantage of drastically reducing the actuation forces and
therefore significantly improving the safety of the robot. To
this end, a counterweight has been included in the cable routing, as illustrated in Figure 5.
Because of the decoupling feature of the routing proposed
for the Z displacement, the counterweight only has to move
along the vertical direction and does not add inertia along the
other axes of motion. It is noted that a similar feature can be
found in [11] but for the decoupling between the vertical
motion and only one other axis of motion.
Z
Y
X
Figure 5. The balancing routing that does not apply a torque on
the bridge.
dL
dC
O
F
MC g
Figure 6. The principle of the balancing system.
•
Fd L = M L gd L = M C gd C, d C = M L d L ,
MC
(1)
where F is the balancing force (which corresponds to the
mass of the load to be balanced, M L), M C is the mass of the
counterweight, g is the gravitational acceleration, d L is the
distance between the point of application of F and the fixed
pivot O, and d C is the variable distance between the center of
mass of the counterweight M C and the fixed pivot O.
In the balancing system, the mass of the counterweight M C
and the location of the force d L are fixed. Therefore, a variation in M L can be compensated for by a change in d C, according to (1). Finally, the vertical actuation is included in the static
balancing routing. An actuator and a ball screw are used to
perform the vertical motion. However, as mentioned above,
the forces required at the actuator are only a fraction of the
weight of the end-effector and payload because the static
weight is always compensated for by the counterweight.
Communicating the User’s Intentions: Force/
Torque Sensor Based on Photointerruptors
i3
142
Variable Balancing Principle
The payload to be handled by the robotic device can be varied.
For instance, the device can be loaded or unloaded. Therefore,
the counterweight should be actively adjusted. To this end, the
counterweight is mounted on a lever and an actuator is used to
displace the counterweight along the lever. This lever is
designed so that its angular motion is relatively small.
Therefore, referring to Figure 6, static equilibrium requires that
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Context
The robotic system presented in this article aims at providing
assistance to a human operator in a variety of ways.
In the interactive mode, the operator makes use of a sensing
handle to apply forces to the device. The input forces are used to
infer the intentions of the operator and provide a simultaneous
intuitive control of the four DoF. Therefore, it is important that
the sensing handle is able to provide a well-conditioned force/
torque signal with low noise and no significant drift.
As pointed out in [12], force/torque sensors based on
strain gauges are sensitive to noise and drift and are not wellsuited for applications in haptics. Instead, optical components can be used in combination with compliant mechanisms. In this work, a 4-DoF force/torque sensor based on
photointerrupters (PIs) was developed by combining singleaxis compliant mechanisms.
Single-Axis Force Sensor
Each single-axis sensor, illustrated schematically in Figure 7,
uses a compliant mechanism to convert the applied force into a
linear displacement. This displacement is then measured
through the gradual interruption of the light beam of a PI,
which is an optical sensor composed of an infrared emitter and
a phototransistor placed face to face. PIs have many advantages
such as noncontact displacement measurement, very low noise
sensitivity, minimal drift, and low cost. The compliant
mechanism is a parallelogram composed of stainless steel spring
leafs built using an electrical-discharge machine. For redundancy, two PIs are attached to the compliant mechanism. The
compliant mechanism has the advantage of being very compliant along the measured direction compared to the compliance
along the other directions, which leads to a decoupled behavior
of the 4-DoF assembly. To protect the flexures from any potential overload along the sensor axis, inner mechanical stops are
included. Also, to finely adjust the position of the PIs, the latter
are installed on a mobile bar. This mobile part is installed on
compliant rings and pulled by screws, allowing for fine adjustment of the position using two adjustment screws.
Sensor Assembly
To measure the applied forces, the sensing handle combines a
minimal number of four single-axis force sensors, mounted as
shown in Figure 8. The mechanical assembly is composed of a
pair of sensors mounted in parallel (A) combined with two
other sensors mounted in series (B and C), as illustrated on the
simplified schematic of Figure 8, in which the compliant axis
of each sensor is represented by a prismatic joint. While the
single-axis sensors (B) and (C) each measure one force acting
along their respective axis ( Fx and Fz and respectively), the
pair of sensors mounted in parallel (A) acts as a bar pivoting
about its center, allowing a torque measurement as well as a
force measurement. This serial assembly of three components
forms the link between the manipulated part of the handle and
its fixed part. In other words, the handle is attached to the pair
of sensors (A) by two rotational joints, while the last single-axis
sensor (C) is rigidly attached to the base of the handle.
Sensing Handle
To provide safe and intuitive control of the assistive device,
the handle is mounted on the exterior vertical beam of the
C-shaped effector, at a safe distance from the payload. The
handle dimensions have been chosen to satisfy the ergonomic
standards specified in [13]. Finally, two pairs of through beam
photoelectric sensors are added to the handle to detect the
presence of the operator’s hands on each side of the handle, as
prescribed by safety norms for hands-on control mode [5].
The sensing handle is shown in Figure 2, where it is referred
to as the control handle.
For comparison purposes, an example of the force signal
obtained from the sensing handle proposed here is plotted in
Figure 9 together with a signal obtained from a commercial
six-axis force/torque sensor based on strain gauges. It can be
observed that the noise is considerably lower with the force/
torque sensor based on PIs. It was also verified experimentally that the drift is significantly reduced with the sensing
handle based on PIs, when compared with the sensor based
on strain gauges.
autonomous mode, the robot moves by itself, for instance, to
fetch a part and present it to the operator. In interactive mode,
the operator guides the motion of the robot using the sensing
handle. Finally, in passive mode, the robot is powered off and
the operator is moving the end-effector. This mode can be
used in case of a failure of the controller or actuators.
In autonomous mode, the robot is controlled using a conventional proportional integral derivative controller, which is
not discussed in this article. In interactive mode, a control
scheme based on admittance control is implemented.
Admittance control accepts a force as a measured input and
produces a displacement (see [14] and [15]). This is in contrast with impedance control, which accepts a displacement as
a measured input and produces a force. Impedance control
Spring Leafs
Mobile Bar
Mechanical Stop
Adjustment
Screw
PI
Figure 7. A schematic representation of one of the single-axis
force sensors.
r
A
C
B
Z
Y
C
X
A
B
Intuitive Interaction: Inference
and Control Algorithms
Three motion modes are considered here, namely autonomous mode, interactive mode, and passive mode. In
Figure 8. A CAD model and schematic representation of the
assembled housings of the sensing handle.
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Because the manipulation of large and heavy payloads is
considered here, large inertia and friction forces are expected,
making an impedance controller inappropriate. On the other
hand, an admittance controller is well suited for such applications and, hence, the latter is used here. The one-dimensional
admittance equation is written as [14]
30
20
10
Force (N)
0
fH = m (xp - xp 0) + c (xo - xo 0) + k (x - x 0),
(2)
-10
-20
-30
-40
-50
0
Commercial Sensor
PI Sensor
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Time (s)
Figure 9. The force signal obtained with the proposed sensor and
a commercial force/torque sensor.
where fH is the interaction force, i.e., the force applied by the
human operator, m is the virtual mass, c is the virtual damping, k is the virtual stiffness, x 0 is the equilibrium point, and
x, xo , and xp are respectively the position, velocity and acceleration. The virtual parameters (mass, damping, and stiffness)
correspond to the prescribed behavior perceived by the user.
Since it is desired to simulate free motion, the stiffness, k, as
well as the desired position, x 0, the desired velocity, xo 0 , and
the desired acceleration, xp 0 , are set to zero. The admittance
equation is then rewritten as
fH = mxp + cxo .
The trajectory to be followed by the robot can be prescribed
as a desired position, x d, or as a desired velocity, xo d . For
velocity control, the desired velocity can be written, in the
Laplace domain, as
Velocity
Human
Force
Xo d (s) =
Robot
Force Sensor
Measured Force
Controller
vd
Admittance
Model
vd
(3)
Saturation
and Limits
Velocity
Controller
Command
Measured
Velocity
FH (s)
FH (s) /c
=
= FH (s) H (s),
ms + c
m s +1
c
(4)
where Xo d (s) is the Laplace transform of xo d, FH (s) is the
Laplace transform of fH , and s is the Laplace variable.
Velocity control is used here, similarly to what was presented
in [16]–[18]. Indeed, with position control, the robot would
be attracted to a given reference position, which does not represent the desired behavior. A discretized desired velocity is
obtained with a zero-order-hold (a bilinear discretization
could also be used) and is represented by
xo d (k) = ;
fH (k) - cxo d (k - 1)
E Ts + xo d (k - 1),
m
(5)
where fH (k) is the interaction force at time step k, xo d (k) is
the desired velocity, and Ts is the sampling period. The
desired acceleration at time step k, noted xp d (k), is then represented by
Mechanism
xp d (k) =
fH (k) - cxo d (k - 1)
.
m
(6)
Figure 10. The control scheme using a velocity controller.
represents the vast majority of applications in haptics literature, while admittance control is less commonly used.
However, impedance control is most appropriate for devices
with low inertia and friction since the user will inevitably feel
these forces.
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Figure 10 presents the control scheme used in this work
where a proportional controller is used as the velocity controller. No derivative gain is used to improve robustness (the
acceleration signal is typically noisy), and no integral gain is
used because the behavior to an operator input would then
depend on the error history and would be counterintuitive.
The transfer function between the input force and the output velocity corresponds to that of a first-order system, given
in (4), where the parameters are the virtual mass ^mh and the
virtual damping ^ c h . One has
fH
Admittance
H (s) = 1/c .
m s +1
c
(7)
c v = c f - a a exp d e
c v = c f + a d exp d e
for acceleration
for deceleration
Desired Motion
x˙d
With the transfer function H (s), it becomes apparent that the
virtual damping affects the steady state value of the response
while the ratio of the virtual mass over the virtual damping
affects the dynamics (it changes the pole of the first-order system). It is recognized in the literature that the damping parameter has a greater influence on human perception than the virtual mass [16]. When the admittance parameters (virtual mass
and virtual damping) are set to high values, a larger force is
generally required to move the robot at a given velocity and/or
acceleration. However, it is easier to perform fine movements
since the robot is less reactive and interaction is thus smoother.
When the parameters are set to low values, it is then generally
easy to move the robot at high velocity and/or acceleration but
more difficult to perform fine movements. In summary, there
is a compromise between the force required to move the robot
and the ability to perform fine movements. This is the main
drawback of a fixed admittance control. Therefore, in this
work, a variable admittance control approach is used. The
objective of variable admittance control is to adjust the admittance parameters according to the inferred human intentions.
In other words, high admittance parameters are desired when
the operator performs fine movements, while lower values of
the parameters should be used when movements involving
large accelerations are performed.
The approach used here is presented in more detail in [19].
First, the intentions of the operator are inferred by computing
the desired acceleration using (6) and by applying the scheme
shown in Figure 11. Admittance parameters are then adjusted
accordingly, as explained in the following.
When acceleration is desired, both virtual damping and
virtual mass should be decreased. Although several solutions
are possible, here the virtual mass to virtual damping ratio is
kept constant, i.e., maintained at the default values. The virtual mass to virtual damping ratio being constant, the dynamics of the device will remain similar [see (7)], which is more
intuitive for the operator.
When a deceleration is desired, ideally, the damping
should be increased while the mass is decreased. To produce
this behavior while maintaining continuity of the parameters,
an exponential function is used to compute the virtual mass.
With this approach, the designer can choose a minimum virtual mass to virtual damping ratio and set a transition
smoothness parameter.
Equations for the variable damping and variable mass are
heuristically chosen as
(8)
x¨d
Sign
Sign
=?
Yes
Accelerate
at x¨d
No
Inferred Human Intention
Decelerate
at x¨d
Figure 11. The scheme used to infer human intentions.
Figure 12. The end-effector of the prototype of the 4-DoF robotic
assistive device.
and
m f cv
for acceleration
cf
mf
^ 1 - b ^ 1 - e -c^c v -c f hhh c v for deceleration , (9)
mv =
cf
mv =
where m v is the effective virtual mass, c v is the effective virtual damping, m f is the default virtual mass, c f is the default
virtual damping, b ^0 < b < 1h is a parameter used to adjust
the steady state virtual mass to virtual damping ratio, c is a
parameter used to adjust the smoothness with which the
ratio changes, and a a and a d are parameters to be tuned. In
practice, for a given maximum magnitude of xp d, denoted
exp d e max, a rough estimate of a a and a d can be obtained by
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which uses a cable. All closed loops are driven using brushless
servo actuators mounted on the fixed frame. Most mechanical
components are composed of steel.
The fourth DoF (vertical motion) uses the routing presented
in Figure 5 for static balancing. A steel cable is used for the vertical motion because of the large forces involved. The balancing
is implemented using a pivot and lever with a counterweight
that can be moved along the lever, according to the principle
explained in Figure 6. The balancing system can be seen on the
right-hand side of the prototype in Figures 1 and 13.
Figure 13. A prototype of the 4-DoF robotic assistive device.
(Photo courtesy of the Laval University Robotics Laboratory.)
Figure 14. The execution of the drawing task with the prototype.
(Photo courtesy of the Laval University Robotics Laboratory.)
preventing c v from reaching the minimum, c min, or maximum, c max, allowed damping
c f - c min
exp d e max
c max - c f
.
ad .
exp d e max
aa
.
(10)
Performance Demonstration: Prototype
Development and Experiments
A prototype of the 4-DoF robotic assistive device proposed in
this article was built at Laval University as part of a collaborative research initiative between the Laval University Robotics
Laboratory and the General Motors Global Research Centre.
The prototype includes all the innovations presented in the
previous sections, namely the closed-loop transmission routings, the variable static balancing principle, the sensing handle
based on PIs, and the novel control algorithms. It was developed in the context of assembly line assistance for the automotive industry. Photos of the prototype are shown in Figures 12
and 13. The prototype has a payload capability of 113 kg, a
workspace of 3.3 (L) # 2.15 (W) # 0.52 (H) m, and a rotational range of motion of 120˚ about the vertical axis.
Mechanical Implementation
The closed-loop mechanical transmissions are implemented
using timing belts for all DoF except for the vertical motion,
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Controller Hardware
The controller of the assistive robotic device was built following
a proven configuration used in industrial robotics. In this configuration, a personal computer (PC) is used as a central unit
while a programmable logic controller (PLC) is used to monitor all components (including the PC) and deal with safety/reliability. One of the advantages of this configuration is to separate
the software part of the controller from the hardware.
The hardware is composed of off-the-shelf components,
namely a dual-core 3-GHz PC, a Sensoray 626 acquisition
card, and an Allen-Bradley MicroLogix 1769 PLC. The actuators are controlled using Copley Xenus XTL-230-40 drives.
Path planning algorithms and torque calculations are executed by the PC.
Simulink and RT-Lab are used to program the control
algorithms. The PC/PLC combination together with RT-Lab
leads to a stable, robust and yet flexible controller, running at
a servo rate of 500 Hz.
Performance
Videos demonstrating the features and the effectiveness of the
prototype can be found at: http://robot.gmc.ulaval.ca/en/
research/theme507.html. Photos taken during the experiments are also shown in Figures 14 and 15. Among other
experiments, a drawing task was used to test the dexterity and
fine motion control of the prototype (see Figure 14). The
drawing task consisted of starting from a given point, advancing for 1.5 m, avoiding an obstacle by turning 90˚, advancing
1.25 m, and then moving to the drawing board by performing
a 1-m lateral displacement. Finally, the operator had to follow
a maze while tracing the path on a piece of paper with a pen
mounted on the robot at 1.4 m from the handle. The performances obtained with this task [19] are a clear demonstration
of the control capabilities.
Other experiments involved the lifting and manipulation of
heavy payloads of up to 100 kg. Barbells were used to emulate
the payloads, as shown in Figure 15. During such typical cooperation tasks, the average power used by the robot is approximately 390 W. This low power resulted due to static balancing.
In addition to providing safety, static balancing allows the payload to be handled manually in all DoF even in the occurrence
of a failure (passive mode of motion). In the latter mode, demonstrated in the videos, the brakes are released and the mechanism is moved manually. Although not ergonomically sustainable, this mode of operation is very important because it
Figure 15. The prototype handling a heavy payload. (Photo
courtesy of the Laval University Robotics Laboratory.)
provides the ability to manually remove the robot from a given
continuous operation (an assembly line) in case of failure.
Conclusion
This article presents the design and control of a novel 4-DoF
assistive robot for the handling of large payloads. In this
design, the actuators are fixed and a set of decoupled cable/belt
closed-loop routings are used to produce the motion of the
end-effector. Additionally, static balancing is used to drastically
reduce actuator power and torque. A novel force/torque sensor
is also integrated in the robot, and control algorithms based on
variable admittance are used. A full-scale prototype integrating
all of the above concepts was built and tested. The low-inertia
and low-power design combined with drift-free and low-noise
force/torque measurements as well as effective control algorithms lead to a very intuitive physical human-robot interaction, as shown in the supporting videos.
Acknowledgments
The authors would like to acknowledge the financial support
of the Natural Sciences and Engineering Research Council of
Canada (NSERC, Grant RDCPJ 334898) and of General
Motors of Canada.
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Clément Gosselin, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: gosselin@gmc.
ulaval.ca.
Thierry Laliberté, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: thierry@gmc.
ulaval.ca.
Boris Mayer-St-Onge, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: boris@gmc.ulaval.ca.
Simon Foucault, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: foucault@gmc.
ulaval.ca.
Alexandre Lecours, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: alexandre.
lecours.1@ulaval.ca.
Vincent Duchaine, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: vincent.duchaine@
etsmtl.ca.
Noémie Paradis, Department of Mechanical Engineering,
Université Laval, Québec, Canada. E-mail: noemie.paradis.1@
ulaval.ca.
Dalong Gao, GM Global Research, Warren, Michigan, USA.
E-mail: dalong.gao@gm.com.
Roland Menassa, GM Global Research, Warren, Michigan,
USA. E-mail: roland.menassa@gm.com.
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