.png){kind=icon}
返回
Agile autonomous guidance using spatial value functions
.png){kind=icon}
PRE
AI
DOI：10.1016/j.conengprac.2010.02.013.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper describes an autonomous guidance system based on receding horizon (RH) optimization. The system is integrated around a spatial, state-dependent cost-to-go (SVF) function that is computed as an approximation to the value function associated with the optimal trajectory planning problem. The function captures the critical interaction between the vehicle dynamics and environment, thereby resulting in tighter coupling between planning and control. The consistency achieved between the RH optimization and the SVF enables a more rigorous implementation of the RH framework to autonomous vehicle guidance. The paper describes the overall approach along flight experimental results obtained in an Interactive Guidance and Control Laboratory. (c) 2010 Elsevier Ltd. All rights reserved.
Keyword:
UAV
Autonomy
Guidance
Optimization
Trajectory
Cost-to-go
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.6
论文数:
5.7K
被引数:
1.1W
机构
暂无机构信息
引用论文
Laser-induced time-resolved luminescence of rare-earth elements in scheelite白钨矿中稀土元素的激光诱导时间分辨发光
.png){kind=icon}
PREAI
分享收藏Does it take older adults longer than younger adults to perceptually segregate a speech target from a background masker?在感知上将语音目标与背景掩蔽器隔离开来是否需要老年人比年轻人更长的时间？
PREAI
分享收藏
分享收藏分享收藏Towards a typology of the mares sandstone of Mallorca. Concept, characterization and stratigraphy
OAAI
OAAI分享收藏
查看更多内容