Automated Task-Based Synthesis and Optimization of Field Robots

Abstract

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 extensibility is a key contribution of the system. A key challenge is effectively optimizing multiple performance metrics; we present a method called Requirement Prioritization that guides the evolutionary algorithm through the design space. We apply Darwin2K to a robot synthesis tasks that includes synthesis of robot kinematics, dynamics, structural geometry, and actuator selection to meet and optimize multiple performance requirements.