지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2009.8
- 수록면
- 1,006 - 1,011 (6page)
이용수
초록· 키워드
오류제보하기
This paper presents a new approach for navigation control of an autonomous blimp using an intelligent reinforcement learning algorithm. Compared to the traditional model based control methods, this control strategy does not require a dynamic model of the blimp, which provides huge advantage in many practical situations where the blimp system model is either hard to acquire or too complicated to apply. As the blimp will acquire a range of the pilot skills through a learning and rewarding mechanism during actual navigation trials, it can automatically account for the environmental changes during the navigation. The simulation data generated from a Webots Robotics Simulator (WRS) demonstrate satisfactory results for planar steering motion control. Reinforcement learning based blimp control is shown to be a promising and effective solution for autonomous navigation tasks.
목차
Abstract
1. INTRODUCTION
2. RELATED WORK
3. REINFORCEMENT LEARNING
4. Q-LEARNING FOR BLIMP CONTROL
5. EXPERIMENTAL EVALUATION
6. SUMMARY AND CONCLUSION
REFERENCES
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