Automated Synthesis and Optimisation of Robot Configurations: An Evolutionary Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{leger:1999:thesis,
  author =       "Chris Leger",
  title =        "Automated Synthesis and Optimisation of Robot
                 Configurations: An Evolutionary Approach",
  school =       "The Robotics Institute, Carnegie Mellon University",
  year =         "1999",
  address =      "Pittsbugh, PA 15213, USA",
  month =        "9 " # dec,
  note =         "CMU-RI-TR-99-43",
  keywords =     "genetic algorithms, genetic programming, Darwin2K",
  URL =          "http://www.ri.cmu.edu/pub_files/pub2/leger_patrick__chris__1999_1/leger_patrick__chris__1999_1.ps.gz",
  URL =          "http://www.ri.cmu.edu/pub_files/pub2/leger_patrick__chris__1999_1/leger_patrick__chris__1999_1.pdf",
  size =         "234 pages",
  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

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