s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

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.

There exists a need for manipulators that are more flexible and reliable than the current fixed configuration manipulators. Indeed, robot manipulators can be easily reprogrammed to perform different tasks, yet the range of tasks that can be performed by a manipulator is limited by its mechanical structure. In remote and hazardous environments, such as a nuclear facility or a space station, the range of tasks that may need to be performed often exceeds the capabilities of a single manipulator. Moreover, it is essential that critical tasks be executed reliably in these environments. To address this need for a more flexible and reliable manipulator, we propose the concept of a rapidly deployable fault tolerant manipulator system. Such a system combines a Reconfigurable Modular Manipulator System (RMMS) with support software for rapid programming, trajectory planning, and control. This allows the user to rapidly configure a fault tolerant manipulator custom-tailored for a given task. This ...

This paper proposes a method for task based design of modular robotic systems using Genetic Algorithms (GA). We introduce a 3D kinematic description for modular serial manipulators and a two-level GA to optimize their topology from task specifications. Revolute and prismatic joints are considered and the number of DOF is let to the GA to determine (allowing redundant manipulators). The upper-level GA is dedicated to the topology evolution and uses the lower-level GA to search for inverse kinematics problem solutions. The topology is evolved for adaptation to a global task constituted by several required end effector configurations (subtasks). The implemented GA optimizes several performance criteria under constraints. To illustrate the method capacities, an example is presented for a redundant manipulator in a cluttered workspace. 1 Introduction Considering the task diversity in modern robotics, new solutions are required to answer the complex issue of adaptation. Modular Robotic Syst...

This paper proposes an Adaptative Multi-Chromosome Evolutionary Algorithm (AMEA) to perform task based design of modular robotic systems. The kinematic design is optimized by the AMEA which uses both binary and real encoding (for kinematic and configuration parameters). In the problem considered for illustration, the robotic system consists in a mobile base and a manipulator arm which may be built with serially assembled link and joint modules. The manipulator has to reach a predefined set of goal frames in a 3D cluttered environment. Its design is evaluated with geometric and kinematic performance measures. Optimization results for a 3D task are given and compared with a Simple Genetic Algorithm. They clearly show the superiority of the Multi-Chromosome representation and adaptive operators in term of computing time and criteria optimization performance. 1 Introduction Considering the task diversity in modern robotics, new solutions are required to answer the complex issue of adapta...

We present Darwin2K, a widely-applicable, extensible software tool for synthesizing and optimizing robot configurations. The system uses an evolutionary optimization algorithm that is independent of task, metrics, and type of robot, enabling the system to address a wide range of synthesis problems. Darwin2K can synthesize fixed-base and mobile robots (including free-flying robots, mobile manipulators, modular robots, and multiple or bifurcated manipulators), and includes a toolkit of simulation and analysis algorithms which are useful for many synthesis tasks. Some of these capabilities, such as dynamic simulation, are novel in automated synthesis of robots. An extensible software architecture enables new synthesis tasks to be addressed while maximizing use of existing system capabilities; this

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