Automated Synthesis and Optimisation of Robot Configurations: An Evolutionary Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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

Citations