The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
로봇을 만드는 것은 일반적으로 어려운 것으로 간주됩니다. 왜냐하면 설계자는 로봇과 환경 사이의 상호 작용을 예측해야 할 뿐만 아니라 그에 따른 문제도 처리해야 하기 때문입니다. 최근에는 로봇 컨트롤러를 합성하기 위한 진화적인 알고리즘이 제안되었습니다. 그러나 제어 시스템의 성능은 몸체 크기, 바퀴 반경, 모터 시상수 등을 포함할 수 있는 다른 하드웨어 매개변수(로봇 몸체 계획)에 따라 달라지기 때문에 제어 시스템을 발전시키는 것만으로는 충분하지 않습니다. 따라서 이상적으로는 로봇 몸체 계획 자체도 진화된 로봇이 달성할 것으로 예상되는 작업에 적응해야 합니다. 본 논문에서는 컨트롤러와 본체를 포함한 완전한 로봇 시스템을 발전시켜 피트니스 관련 작업을 달성하기 위한 하이브리드 GP/GA 프레임워크를 제시합니다. 개발된 시스템의 성능을 평가하기 위해 고정된 로봇 본체 계획과 함께 이를 사용하여 처음에는 다양한 작업을 위한 컨트롤러를 발전시킨 다음 완전한 로봇 시스템을 발전시킵니다. 실험 결과는 우리 시스템의 가능성을 보여줍니다.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Wei-Po LEE, "Evolving Autonomous Robot: From Controller to Morphology" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 2, pp. 200-210, February 2000, doi: .
Abstract: Building robots is generally considered difficult, because the designer not only has to predict the interactions between the robot and the environment, but also has to deal with the consequent problems. In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory enough just to evolve the control system, because the performance of the control system depends on other hardware parameters -- the robot body plan -- which might include body size, wheel radius, motor time constant, etc. Therefore, the robot body plan itself should, ideally, also adapt to the task that the evolved robot is expected to accomplish. In this paper, a hybrid GP/GA framework is presented to evolve complete robot systems, including controllers and bodies, to achieve fitness-specified tasks. In order to assess the performance of the developed system, we use it with a fixed robot body plan to evolve controllers for a variety of tasks at first, then to evolve complete robot systems. Experimental results show the promise of our system.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_2_200/_p
부
@ARTICLE{e83-d_2_200,
author={Wei-Po LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Evolving Autonomous Robot: From Controller to Morphology},
year={2000},
volume={E83-D},
number={2},
pages={200-210},
abstract={Building robots is generally considered difficult, because the designer not only has to predict the interactions between the robot and the environment, but also has to deal with the consequent problems. In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory enough just to evolve the control system, because the performance of the control system depends on other hardware parameters -- the robot body plan -- which might include body size, wheel radius, motor time constant, etc. Therefore, the robot body plan itself should, ideally, also adapt to the task that the evolved robot is expected to accomplish. In this paper, a hybrid GP/GA framework is presented to evolve complete robot systems, including controllers and bodies, to achieve fitness-specified tasks. In order to assess the performance of the developed system, we use it with a fixed robot body plan to evolve controllers for a variety of tasks at first, then to evolve complete robot systems. Experimental results show the promise of our system.},
keywords={},
doi={},
ISSN={},
month={February},}
부
TY - JOUR
TI - Evolving Autonomous Robot: From Controller to Morphology
T2 - IEICE TRANSACTIONS on Information
SP - 200
EP - 210
AU - Wei-Po LEE
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2000
AB - Building robots is generally considered difficult, because the designer not only has to predict the interactions between the robot and the environment, but also has to deal with the consequent problems. In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory enough just to evolve the control system, because the performance of the control system depends on other hardware parameters -- the robot body plan -- which might include body size, wheel radius, motor time constant, etc. Therefore, the robot body plan itself should, ideally, also adapt to the task that the evolved robot is expected to accomplish. In this paper, a hybrid GP/GA framework is presented to evolve complete robot systems, including controllers and bodies, to achieve fitness-specified tasks. In order to assess the performance of the developed system, we use it with a fixed robot body plan to evolve controllers for a variety of tasks at first, then to evolve complete robot systems. Experimental results show the promise of our system.
ER -