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
자율 로봇 개발을 위해 IEWL(Incremental Evolution with Learning)이 제안되었으며, 복잡한 작업을 수행하기 위해 실제 이동 로봇을 사용하여 방법의 타당성을 평가합니다. 환경의 장애물을 피하여 목표물에 접근하는 복잡한 작업을 위한 제어 시스템의 개발은 점진적으로 2단계로 진행됩니다. 첫 번째 단계에서는 환경의 장애물을 피하기 위해 컨트롤러가 개발됩니다. 1단계에서 습득한 지식을 활용하여 2단계에서는 환경의 장애물을 피하여 목표물체에 접근하는 컨트롤러를 개발한다. 점진적인 진화와 함께 학습을 사용하는 것은 진화하는 인구의 다양성을 유지하는 데 유익한 것으로 밝혀졌습니다. 주어진 작업에 대해 IEWL에서 개발한 컨트롤러와 IENL(Incremental Evolution Without Learning)로 개발한 두 컨트롤러의 성능을 비교합니다. 실험 결과는 IEWL이 컨트롤러를 개발할 때 강력한 성능이 달성된다는 것을 보여줍니다.
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Md. Monirul ISLAM, Kazuyuki MURASE, "Incremental Evolution with Learning to Develop the Control System of Autonomous Robots for Complex Task" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 7, pp. 1118-1129, July 2002, doi: .
Abstract: Incremental evolution with learning (IEWL) is proposed for the development of autonomous robots, and the validity of the method is evaluated with a real mobile robot to acquire a complex task. Development of the control system for a complex task, i.e., approaching toward a target object by avoiding obstacles in an environment, is incrementally carried out in two-stage. In the first-stage, controllers are developed to avoid obstacles in the environment. By using acquired knowledge of the first-stage, controllers are developed in the second-stage to approach toward the target object by avoiding obstacles in the environment. It is found that the use of learning in conjunction with incremental evolution is beneficial for maintaining diversity in the evolving population. The performances of two controllers, one developed by IEWL and the other developed by incremental evolution without learning (IENL), are compared on the given task. The experimental results show that robust performance is achieved when controllers are developed by IEWL.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_7_1118/_p
부
@ARTICLE{e85-d_7_1118,
author={Md. Monirul ISLAM, Kazuyuki MURASE, },
journal={IEICE TRANSACTIONS on Information},
title={Incremental Evolution with Learning to Develop the Control System of Autonomous Robots for Complex Task},
year={2002},
volume={E85-D},
number={7},
pages={1118-1129},
abstract={Incremental evolution with learning (IEWL) is proposed for the development of autonomous robots, and the validity of the method is evaluated with a real mobile robot to acquire a complex task. Development of the control system for a complex task, i.e., approaching toward a target object by avoiding obstacles in an environment, is incrementally carried out in two-stage. In the first-stage, controllers are developed to avoid obstacles in the environment. By using acquired knowledge of the first-stage, controllers are developed in the second-stage to approach toward the target object by avoiding obstacles in the environment. It is found that the use of learning in conjunction with incremental evolution is beneficial for maintaining diversity in the evolving population. The performances of two controllers, one developed by IEWL and the other developed by incremental evolution without learning (IENL), are compared on the given task. The experimental results show that robust performance is achieved when controllers are developed by IEWL.},
keywords={},
doi={},
ISSN={},
month={July},}
부
TY - JOUR
TI - Incremental Evolution with Learning to Develop the Control System of Autonomous Robots for Complex Task
T2 - IEICE TRANSACTIONS on Information
SP - 1118
EP - 1129
AU - Md. Monirul ISLAM
AU - Kazuyuki MURASE
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2002
AB - Incremental evolution with learning (IEWL) is proposed for the development of autonomous robots, and the validity of the method is evaluated with a real mobile robot to acquire a complex task. Development of the control system for a complex task, i.e., approaching toward a target object by avoiding obstacles in an environment, is incrementally carried out in two-stage. In the first-stage, controllers are developed to avoid obstacles in the environment. By using acquired knowledge of the first-stage, controllers are developed in the second-stage to approach toward the target object by avoiding obstacles in the environment. It is found that the use of learning in conjunction with incremental evolution is beneficial for maintaining diversity in the evolving population. The performances of two controllers, one developed by IEWL and the other developed by incremental evolution without learning (IENL), are compared on the given task. The experimental results show that robust performance is achieved when controllers are developed by IEWL.
ER -