Automated Synthesis and Optimisation of Robot Configurations: An Evolutionary Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @PhdThesis{leger:1999:thesis,
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author = "Chris Leger",
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title = "Automated Synthesis and Optimisation of Robot
Configurations: An Evolutionary Approach",
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school = "The Robotics Institute, Carnegie Mellon University",
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year = "1999",
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address = "Pittsbugh, PA 15213, USA",
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month = "9 " # dec,
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note = "CMU-RI-TR-99-43",
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keywords = "genetic algorithms, genetic programming, Darwin2K",
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URL = "http://www.ri.cmu.edu/pub_files/pub2/leger_patrick__chris__1999_1/leger_patrick__chris__1999_1.ps.gz",
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URL = "http://www.ri.cmu.edu/pub_files/pub2/leger_patrick__chris__1999_1/leger_patrick__chris__1999_1.pdf",
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size = "234 pages",
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abstract = "Robot configuration design is hampered by the lack of
established, well-known design rules, and designers
cannot easily grasp the space of possible designs and
the impact of all design variables on a robot's
performance. Realistically, a human can only design and
evaluate several candidate configurations, though there
may be thousands of competitive designs that should be
investigated. In contrast, an automated approach to
configuration synthesis can create tens of thousands of
designs and measure the performance of each one without
relying on previous experience or design rules. This
thesis creates Darwin2K, an extensible, automated
system for robot configuration synthesis. This research
focuses on the development of synthesis capabilities
required for many robot design problems: a flexible and
effective synthesis algorithm, useful simulation
capabilities, appropriate representation of robots and
their properties, and the ability to accomodate
application-specific synthesis needs. Darwin2K can
synthesize and optimize kinematics, dynamics,
structural geometry, actuator selection, and task and
control parameters for a wide range of robots. Darwin2K
uses an evolutionary algorithm to synthesize robots,
and uses two new multi-objective selection procedures
that are applicable to other evolutionary design
domains. The evolutionary algorithm can effectively
optimize multiple performance objectives while
satisfying multiple performance constraints, and can
generate a range of solutions representing different
trade-offs between objectives. Darwin2K uses a novel
representation for robot configurations called the
parameterized module configuration graph, enabling
efficient and extensible synthesis of mobile robots, of
single, multiple and bifurcating manipulators, and of
robots with either modular or monolithic construction.
Task-specific simulation is used to provide the
synthesis algorithm with performance measurements for
each robot. Darwin2K can automatically derive dynamic
equations for each robot it simulates, enabling dynamic
simulation to be used during synthesis for the first
time. Darwin2K also includes a variety of simulation
components, including Jacobian and PID controllers,
algorithms for estimating link deflection and for
detecting collisions; modules for robot links, joints
(including actuator models), tools, and bases (fixed
and mobile); and metrics such as task coverage, task
completion time, end effector error, actuator
saturation, and link deflection. A significant
component of the system is its extensible
object-oriented software architecture, which allows new
simulation capabilities and robot modules to be added
without impacting the synthesis algorithm. The
architecture also encourages re-use of existing toolkit
components, allowing task-specific simulators to be
quickly constructed. Darwin2K's synthesis algorithm,
simulation capabilities, and extensible architecture
combine to allow synthesis of robots for a wide range
of tasks. Results are presented for nearly 150
synthesis experiments for six different applications,
including synthesis of a free-flying 22-DOF robot with
multiple manipulators and a walking machine for
zero-gravity truss walking. The synthesis system and
results represent a significant advance in the
state-of-the-art in automated synthesis for robotics.",
- }
Genetic Programming entries for
Chris Leger
Citations