A study of cooperative control of self-assembling robots in space with experimental validation
ABSTRACT Modular self-assembling on-orbit robotic and satellite systems can be more reliable, have lower launch costs, and be more easily repaired and refueled. However, when individual modules assemble, many challenges and opportunities make the control of the assembled system complex. These issues include changes in inertial properties, and redundancy of actuators and sensors. Optimal control methods may be used to coordinate the control of the modules after assembly, insure good performance, and best utilize the combined resources of the assembly of modules. Simulation and experimental results compare this Cooperative algorithm's performance to that of an approach in which the control of the individual modules is not coordinated. Cooperative optimal control methods prove well-suited for controlling redundant, modular space systems.
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ABSTRACT: Modular self-assembling on-orbit robots have the potential to reduce mission costs, increase reliability, and permit on-orbit repair and refueling. Modules with a variety of specialized capabilities would self-assemble from orbiting inventories. The assembled modules would then share resources such as power and sensors. As each free-flying module carries its own attitude control actuators, the assembled system has substantial sensor and actuator redundancy. Sensor redundancy enables sensor fusion that reduces measurement error. Actuator redundancy gives a system greater flexibility in managing its fuel usage. In this paper, the control of self-assembling space robots is explored in simulations and experiments. Control and sensor algorithms are presented that exploit the sensor and actuator redundancy. The algorithms address the control challenges introduced by the dynamic interactions between modules, the distribution of fuel resources among modules, and plume impingement.Autonomous Robots 01/2011; 31:209-221. · 1.91 Impact Factor
Abstract—Modular self-assembling on-orbit robotic and
satellite systems can be more reliable, have lower launch
costs, and be more easily repaired and refueled.
However, when individual modules assemble, many
challenges and opportunities make the control of the
assembled system complex. These issues include changes
in inertial properties, and redundancy of actuators and
sensors. Optimal control methods may be used to
coordinate the control of the modules after assembly,
insure good performance, and best utilize the combined
resources of the assembly of modules. Simulation and
experimental results compare
algorithm’s performance to that of an approach in which
the control of the individual modules is not coordinated.
Cooperative optimal control methods prove well-suited
for controlling redundant, modular space systems.
modular approach to self-assembling on-orbit robotic
systems and spacecraft has great potential for reducing
costs, increasing long-term reliability, and providing the
means for rapid repair and refueling. Such a spacecraft or
space robot would consist of an assembly of a number of
modules, each designed for specific tasks such as propulsion,
payload, fuel storage, etc. See Fig. 1. The modules within a
system could share resources such as power, sensors,
computational capabilities, and data. The modules’ small
size and the maintenance of an on-orbit module inventory
enables launching flexibility. Moreover, the possibility of
mass producing simple modules rather than individually
crafting unique satellites offers great potential for design and
production savings . The modular approach also provides
redundancy. The ability to completely replace failed modules
will greatly increase robustness to failure , .
In this concept, system assembly takes place in orbit.
Hence, each module is required to carry sufficient attitude
control actuators (thrusters and reaction wheels) and sensors
to permit free-flying control and docking. These multiple
sensors give the assembled system substantial sensor
redundancy that can be used with sensor fusion techniques to
minimize sensor errors. The actuator redundancy can provide
C. Toglia is a PhD Candidate at “La Sapienza”, Universita’ di Roma,
Rome, Italy (e-mail: email@example.com).
D. Kettler is a Masters student at MIT, Cambridge, MA 02139 USA (e-
F. Kennedy is a Lt Col in the US Air Force and has been attached to the
Defense Advanced Research Projects Agency.
S. Dubowsky is a Professor in the Mechanical Engineering Department,
MIT, Cambridge, MA 02139 USA (e-mail: firstname.lastname@example.org).
the systems with greater agility and flexibility in fuel usage
management. Moreover it enables the introduction of
additional control constraints, for example those related to
Consequently, modular orbiting satellites and robotics
systems have the potential to be simultaneously less
expensive and more responsive, adaptable, and robust to the
failure of one of its module. With these benefits come
significant, and largely unaddressed, control challenges.
These challenges result from the dynamic interactions
between modules, the changing inertial properties, structural
compliance, and sensor and actuator redundancy.
The control of formation flying orbital systems has been
well studied, as well as the control of spacecraft and space
robots maneuvering in close proximity for rendezvous and
docking procedures. Orbital formation flying concepts
distribute the functionality of large spacecraft over a set of
cooperative, smaller, less expensive spacecraft which do not
physically contact each other. Work in this field has focused
on modeling, coordination and control, simulation, and
autonomous formation reconfiguration , , .
However, the elements in these systems do not face the
challenges of physical interaction found in the modular space
robot concept addressed here.
Substantial work has also been done on the dynamics and
control of the rendezvous and docking of spacecraft and
space robots , , , . These works have focused on
the period just before docking when the docking elements
are free-flying or free-floating, or the first few moments after
docking. The assumption generally made in these works is
that, after docking, the control of the system is stable and
well behaved. Moreover, the control of the system after
docking is not adjusted or optimized to account for changed
A Study of Cooperative Control of Self-Assembling Robots in Space
with Experimental Validation
Chiara Toglia, Daniel Kettler, Fred Kennedy, and Steven Dubowsky, Fellow, IEEE
Fig. 1. Image A - two modules assembling. Image B -
assemblies capturing a satellite
mass properties of the joined system. These problems must
be investigated to properly control self-assembling modular
space robots and spacecraft. One of the few studies that have
considered the control of assemblies uses the SPHERES
In this paper, the analytical development of a Cooperative
Control approach, in which control efforts are coordinated
between the modules using linear quadratic regulator (LQR)
methods, is presented. This approach uses optimal control
methods to coordinate the control of the modules after
assembly to insure good stable system performance, and to
best utilize the combined resources. The algorithm balances
trajectory error, plume impingement, total fuel consumption,
as well as the distribution of fuel consumption among
modules in determining actuator commands and thruster
selection. It is important that the system balances the fuel
usage between modules since fuel is difficult to redistribute.
Simulation and experimental results are presented for the
algorithm’s performance and compared to those of a control
approach in which the control of the individual modules is
not coordinated after assembly, called here Independent
Control. The simulations are done for a representative
system consisting of an assembly of nanosatellite modules.
The experimental results are obtained using the MIT Field
and Space Robotics Laboratory (FSRL) Free-Flying Space
Robot (FFR) Testbed , . Both the simulations and
experimental results show the effectiveness of the proposed
control approach. The Cooperative Control approach
performs better than Independent Control, yielding lower
trajectory errors, and lower fuel consumption.
II. SYSTEM MODEL AND ASSUMPTIONS
A number of simplifying assumptions are made in
modeling the system. For clarity, a 2D planar case is
considered, and in both simulations and experiments,
trajectories consist of sequential, not simultaneous,
translation and rotation elements. More complex trajectories
have been tested in simulation but are beyond the scope of
this paper . The small effects of solar pressure, gravity
gradient, and thermal warping are neglected. The orbit
altitude is assumed sufficiently high so that aerodynamic
effects are also negligible. Further, it is assumed that the time
scale for an assembly’s operations is much shorter than the
orbital period, so that the effects of orbital mechanics are not
included. The compliance of the system elements is also
neglected. Finally, the modules are assumed to have thrusters
but not reaction wheels. As a result, the dynamics of the
assembly, or an individual module, may be approximated by
the simple linear equation
where x is the n × 1 state vector and u is the r × 1, where
r = p × m, p is the number of thrusters per module and m is
the number of modules composing the assembly.
Since there is no damping, the A matrix contains simple
integrator dynamics, i.e. zeros and ones. All the information
related to the assembly configuration is contained in the B
matrix: inertial characteristics, number of thrusters, direction
of each thrust, and the relative distance of the thrusters from
the global center of mass, i.e. geometry of the thruster
placement. Using such a compartmentalized approach, the
dynamics and the control strategies can be easily adapted to
any configuration, by updating the B matrix. Simulations
demonstrating this adaptability to multiple modules have
been done and will be covered in future publications.
A. The Control Problem
For a fixed system configuration, a stable and effective
attitude and maneuvering control system can be designed
using well known methods. However, when the individual
modules assemble themselves, a number of factors enter,
making this problem more complex. These include changes
in inertial properties and redundancy of actuators and
sensors. Each module could control itself as if it were
independent, so that control would be distributed and not
cooperative. However, this control algorithm would clearly
be suboptimal and would potentially have robustness issues.
For example, measurement errors and noise, as well as
uncertainty in actuator thrusts could produce control errors
that would cause the individual modules to “fight” against
each other. This results in increased fuel consumption,
higher trajectory errors, and higher internal constraint forces
in the docking mechanism between modules. In extreme
cases the result could be destabilization of the system.
B. The Control Concept and Performance metrics
A more effective way to control the assembled modules,
and minimize the above problems, is Cooperative Control,
i.e. a single well integrated architecture that reflects the
current configuration of modules. Developed below, a
Cooperative Controller provides good performance and
stability while exploiting sensor and actuator redundancy.
Several metrics are used to develop and evaluate this
control approach. The first is the trajectory error on a
selected reference maneuver. The second is the controller’s
fuel consumption. The total amount of fuel consumed and
the control algorithm’s ability to direct fuel usage among
modules are considered. Finally, the magnitude of forces
between connected modules indicates the degree to which
the thrust commands of modules conflict. Coordination of
module control will minimize these forces.
C. Proposed Optimal Cooperative Control Algorithm
Since the system is time-varying linear, a linear quadratic
regulator (LQR) optimal controller is used to minimize
errors during a maneuver while minimizing total fuel usage
and balancing the fuel usage among modules. The cost
function J to be minimized is:
where δx = xdes - x is the trajectory error, δxT(tf) H δx(tf) is the
cost at the terminal time tf, Q, and R are square matrices. The
first term in the integral penalizes errors in following the
trajectory command, while uTRu is the cost on the fuel
consumed by the thrusters. The control strategy has to be
computed ahead of time. The optimal solution is :
where tr = tf – t is the remaining time. The matrix W(tr) is
obtained integrating the Riccati equation in (4) .
The matrix V(tr) can be found by integrating equation (5).
A closed loop control can thus be obtained from (3), using
the time-varying gains W(tr) and V(tr) that are computed a
priori. This controller automatically selects thrusters form
redundant sets to minimize fuel consumption.
In order to address such issues as plume impingement and
the balancing of fuel consumption, the proposed algorithm
adjusts the B matrix. B is a n × r matrix, that can be
decomposed as follows
B is a n × r matrix which translates thruster inputs into net
forces and torques about the principle axes. It describes the
behavior of the system including all thrusters without any
special adjustments. BPIC is a r × r matrix that introduces the
Plume Impingement Constraint (PIC), by removing the
contribution of those thrusters that are poorly positioned.
Thus BPIC acts as a selection matrix: all the non diagonal
elements are zero. The diagonal elements related to unusable
thrusters are zero, while those diagonal elements related to
well positioned thrusters are one. Note that the methods
developed for PIC may also be used to compensate for
malfunctioning thrusters. BFB is a r × r matrix that introduces
a weighting based on a Fuel Balancing (FB) constraint. This
insures balanced fuel consumption among the included
modules so that resource levels stay evenly apportioned. It is
a diagonal matrix whose elements are weights based on
relative fuel levels related to each module. The weighting is
the same for all the thrusters belonging to the same module
and has to be computed at every instant, taking into account
possible leaks of fuel or differences between actual and
commanded thrusts. A different approach to fuel balancing
can be followed if the difference in fuel levels is known a
priori: the fuel usage distribution can be regulated using
different weights in the LQR gains computation: in particular
the R matrix, which is a diagonal matrix, can be written using
weighted gains for each robot, so the fuel distribution is
optimal and computed ahead of time.
Cooperative Control enables the easy incorporation of
additional modules and constraints in an optimal fashion
through the B matrix. An alternative approach is developed
in previous literature . It searches through the thrusters to
assign one thruster to supply each component of force or
torque. The forces and torques to be applied at the center of
mass are computed and the components commanded from
the specified thrusters. This approach is simple, reliable, and
does not require an expensive gain calculation, with a
consequent saving in computational cost. However, it is not
optimal and does not enable the easy addition of constraints
such as Fuel Balancing. Consequently, it is less general than
the Cooperative LQR approach and does not provide means
of minimizing fuel consumption.
A. Case Study: Description and assumption
To facilitate the design of
controllers, the case of two
considered. See Fig. 2. Each
module is equipped with two
thrusters, resulting in a total of
16 thrusters for the assembly.
Commands are continuous.
The modules have a mass of
10 kg, dimensions on the order
of 0.5m, and a maximum
manipulator reach of 20 cm
An assembly composed of two
nanosatellites with identical
characteristics (mass, moment
of inertia, thrusters location
and saturation threshold) is
considered. The assembly
configuration is symmetric,
although the method extends to asymmetric assemblies and
non-identical modules. Manipulators connect the modules.
These manipulators are locked and are assumed rigid. For
simplicity, modules are equipped only with thrusters in these
simulations. The reference trajectory is planar and composed
of two sequential parts: a translation in the y direction,
starting and ending with zero velocity, and a rotational
motion around the axis normal to the plane, Fig. 2. The
mission duration is 30 s, each parts lasts 15 s.
B. Simulation Results
The Cooperative Controller and the Independent
Controller were investigated and compared. Both the control
strategies had good trajectory tracking performance, with the
Cooperative Controller producing slightly better results. Fig.
3 shows a comparison of the tracking performance. The
Independent Controller produces small oscillations when the
system is commanded to keep constant attitude angle,
because of the antagonism of the two independent
controllers. Similarly, a small drift along X and oscillations
about the reference trajectory in the Y direction can be seen
in Fig. 3. Fig. 4 shows that the total fuel consumption for the
Fig. 2. Reference Trajectory.
two controllers is very different: the Independent control
used 82% more fuel than Cooperative Controller. In the
figure, the fuel consumption has been normalized with
respect to the total fuel used under the Cooperative Control.
Adding the Plume Impingement Constraint (PIC) to the
Cooperative Controller did not change performance
significantly. Fuel consumption was roughly the same.
Applying the fuel balancing constraint to the controller
produced the desired inequality in fuel consumption rates as
well as in an increase of the total fuel usage of approximately
6%. This result is not unexpected, since Fuel Balancing
constraints can alter the penalty on overall fuel consumption.
IV. EXPERIMENTAL VALIDATION
To validate the simulation results, an experimental study
of the Cooperative Controller was performed, using the MIT
Field and Space Robotics Laboratory (FSRL) Free-Flying
Space Robotics (FFR) Testbed , . See Fig. 5.
The FFR Testbed consists of two multi-arm, 6.5 kg robots
floating on CO2 bearings to emulate microgravity in two
dimensions. For these tests the robots floated on a 1.3 m x
2.2 m polished granite table. The robots are equipped with
two Scara-type two-joint manipulators, eight thrusters, two
module position sensors that provide position and velocity
data, four manipulator joint angle encoders, and two
force/torque sensors. The robots have 7 DOF in total (2
module DOF’s in translation and 1 DOF in rotation, and 4
DOF’s for the manipulators’ joint motions), all of which are
controllable and observable. Throttled thrust commands are
achieved by commanding individual thrusters with a pulse
width modulated signal. All actuators and sensors are
controlled by an on-board computer and powered by
onboard batteries, so that the robots can work without any
externally connected cables. Under Cooperative Control,
both modules in an assembly are controlled by the
processor on one module. While not currently used, the
additional processor of the remaining module is available
for computationally expensive tasks such as gain
recalculation. The experimental operator can access the
onboard computer using wireless LAN adapters. The
maximum thruster force is approximately 0.1 N.
For the experiments discussed here, the robots’
manipulator end-effectors were magnetically connected
during the tests. The manipulators attempted to maintain
constant module internal geometry during the tests.
However, compliance in the manipulators and in the
connections introduced some flexibility.
A. Cases studied
Two controllers were investigated: Cooperative Control,
and Independent Control. A number of variations on
Cooperative Control, including
Constraint (PIC) and Fuel Balancing (FB) were tested.
For the majority of tests, the reference trajectory is a short
translational maneuver of a distance of 0.75 m using a
constant-velocity step in the Y direction, starting from rest.
When testing the Cooperative Control with, and without
Plume Impingement Constraints, a 90° rotation trajectory
was used, as the simple translational maneuver created
minimum plume impingement potential. In this case, the
robot assembly started at rest and was commanded to follow
a step command in rotational velocity.
B. Performance metrics
The performance of the investigated control methods were
compared using the following metrics.
Fig. 4. Simulation total fuel consumption. The vertical line indicates
the switch from translation to rotation
Fig. 3. Simulation Trajectory Tracking Performance
Fig. 5. FFR Testbed showing two robot modules in an assembly.
Fuel consumption: The total amount of the fuel (CO2
gas) consumed by the individual robots during the test was
estimated from the thruster command history. These values
do not include the CO2 gas used to float the robots.
Trajectory Error: The root mean square error (RMS error
or RMSE) that each controller achieves on the commanded
trajectory was also considered. Position and orientation
errors were collected in addition to their derivatives. The
velocity error metrics were calculated using data from only
the final ⅔ of the duration of a test in order to allow initial
transients to decay.
To evaluate each controller, a total of 10 trials were run
using the appropriate trajectory. The means of collected fuel
usage and trajectory RMS error values were compared for
statistically significant differences using t-tests. In all cases,
statistical significance corresponds to p < 0.05, indicating
that there is a 5% probability that the observed results could
occur when there is no difference in the means.
Table I lists mean fuel consumption and trajectory RMS
error for experimental results on the Cooperative and
Independent Controllers. These results show that the
Cooperative approach is superior. The Cooperative Control
used 43% less fuel (45 g) than the Independent Control
(79 g). See Fig. 6. The change in slope indicates where the
Cooperative Controller reduces fuel consumption after initial
acceleration. Much of this savings resulted because under
Cooperative Control antagonism between the module
propulsion systems is avoided, allowing the assembly to
enter a coasting mode and fire thrusters only occasionally to
maintain trajectory. Under Independent Control, small miss-
alignments and compliance in the connecting links foster
small oscillating fluctuations in assembly geometry and
wasted thrust. The Cooperative Control also had better
trajectory tracking performance (see Fig. 7) with significant
improvements in X RMS error and, most notably, Θ RMS
error where the Cooperative LQR controller reduced mean
error by 91%.
Table II lists mean fuel consumption and trajectory RMS
error for experimental results on the Cooperative and
Cooperative with Plume Impingement Constraint (PIC)
Controllers. While the additional constraint effectively
prevented the use of poorly-positioned thrusters as desired,
there were negligible reductions in trajectory tracking
performance. A significant difference was only found
between the mean Θ RMS errors. However, this difference
was only 3%.
Unexpectedly, the addition of the plume impingement
constraint reduced total fuel consumption by 18%. This
occurs because with PIC the assembly no longer directs
thruster plumes against its own surfaces thereby wasting
thrust and fuel.
Table III lists mean fuel consumption and trajectory RMS
error for experimental results on the Cooperative and
Cooperative with Fuel Balancing (FB) Controllers.
Experimental results demonstrate, with statistical
Fig. 6. Median fuel consumption for Cooperative and Independent Control.
Note the distinctive reduction in slope of the Cooperative line at time 1.5 s.
Fig. 7. Median Y and Θ RMS error for Cooperative and Independent
Control. Y performance is equivalent. Cooperative Control has much
lower Θ error.
COOPERATIVE CONTROL VS INDEPENDENT CONTROL
45 g 79 g <0.001
2.4 cm s-1
2.4 cm s-1
Note p < 0.05 corresponds to statistical significance.
COOPERATIVE CONTROL VS COOPERATIVE PIC CONTROL
147 g <0.001
Note p < 0.05 corresponds to statistical significance.