Large space structures, such as telescopes and spacecraft, should ideally be
assembled directly in space, as they are difficult or impossible to launch
from Earth as a single piece. In several cases, however, assembling these
technologies manually in space is either highly expensive or unfeasible.
In recent years, roboticists have thus been trying to develop systems that
could be used to automatically assemble structures in space. To simplify
this assembly process, space structures could have a modular design, which
essentially means that they are comprised of different building blocks or
modules that can be shifted to create different shapes or forms.
Researchers at the German Aerospace Center (DLR) and Technische Universität
München (TUM) have recently developed an autonomous planner that could be
used to assemble reconfigurable structures directly in space. This system,
introduced in a paper presented at the 2021 IEEE Aerospace Conference, could
allow aerospace engineers and astronauts to assemble large structures in
space and adapt them for specific use cases, reconfiguring them when
necessary.
"Our paper was inspired by the MOSAR project," Ismael Rodriguez, Adrian
Bauer and Maximo Roa, three of the researchers who carried out the study,
told TechXplore via email. "In this project, we study modular assemblies for
creating the next generation of satellites. Imagine that a satellite can be
created as an array of cubic modules (just like Lego pieces) and the
satellite can easily be reconfigured in space for maintenance or to update
its hardware."
The assembly or reconfiguration of satellites on-orbit should be performed
by a robotic arm. In their paper, Rodriguez, Bauer, Roa and their colleagues
introduced a planner that could plan the movements of this robotic arm. They
specifically utilized a hybrid planner, a type of planner that has often
been used to achieve autonomous robot-based manufacturing.
"The system we created consists of two layers, a symbolic one and a physical
one," the authors said. "Given the exponential number of all possible
solutions, it is very costly to verify the kinematics for each one of them.
To quickly rule out unfeasible solutions, the symbolic layer verifies that
possible solutions fulfill certain conditions such as connectivity of the
satellite before passing them to the physical layer."
The 'symbolic layer' of the planner developed by the researchers also sets a
series of rules that are acquired by the physical layer. For instance, if
the system tries to perform an action that fails in the physical layer, it
stores this information and avoids symbolic solutions that involve the same
action.
The system's physical layer, on the other hand, utilizes kinematic
simulations to execute a given symbolic solution. This allows the system to
verify that individual assembly steps are actually executable by the robotic
arm, while also considering its unique features and characteristics (e.g.,
its reachability, dexterity, payload and motion constraints).
"In our opinion, the biggest achievement of this work is the development of
the system that generates symbolic rules from experience in the physical
layer," Rodriguez, Bauer and Roa said. "We used different techniques,
including a binary prediction tool, to predict which symbolic actions were
kinematically feasible in the given environment."
The binary prediction tool used by the researchers cuts the time necessary
to plan the robotic arm's movements, in some cases reducing it by almost
50%. Moreover, by simulating different scenarios, it ensures that specific
movements are kinematically executable.
"This tool also simplifies the planning process, which would be
painstakingly difficult for a human, especially for manually checking the
validity of a given sequence of motions," Rodriguez, Bauer and Roa said.
The researchers verified their planner in a series of tests, specifically
evaluating its ability to disassemble parts of a modular structure and
reassemble them into a new configuration. In these tests, their system
achieved remarkable results and was also found to be highly adaptable, as it
enabled the assembly of robots with different sets of skills, in scenarios
with simulated hardware failures.
In the future, the autonomous planning system developed by Rodriguez, Bauer,
Roa and their colleagues could simplify the assembly and reconfiguration of
large-scale structures in space. Meanwhile, the team would like to extend
the scope of their system's physical layer, by considering both kinematic
and dynamic restrictions.
"For instance, some optimizations could be included to reduce the
disturbances experienced by a satellite when the robotic arm is moving a
cube around," Rodriguez, Bauer and Roa explained. "Another research
direction we would like to explore in the future is the use of a pattern
recognition algorithm, which could identify sub-structures that have already
been considered, so that we can reuse the already computed subplans to save
time during the generation of a new plan."
Reference:
Ismael Rodriguez et al, Autonomous Robot Planning System for In-Space
Assembly of Reconfigurable Structures, 2021 IEEE Aerospace Conference
(50100) (2021). DOI:
10.1109/AERO50100.2021.9438257