Cooperative Localization and Mapping
Cooperative Localization and Mapping
复制标题
合作定位与测绘
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
David Pacifico
中科院分区：
文献类型：
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作者：
J. Stipes;Robert Hawthorne;D. Scheidt;David Pacifico
要在特征丰富的GPS区域实现自主的多车辆控制，需要智能体具有生成和共享地图的能力，这些地图的细节足以在复杂环境中成功操作。此外，通过利用群中所有成员获得的信息，群中机器人车辆之间的行为协调大大增强。这种信息交换的表示和机制以及利用这种信息的控制算法是自主多车辆控制的关键组成部分。在特征丰富的GPS拒绝环境中，利用自适应势场(SPF)和分布式控制算法实现自适应、协作行为是本文的重点。具体地说，所描述的控制算法已经与领先的同时定位和测绘(SLAM)方法相结合，以产生包括探索和测绘在内的稳健的多机器人行为
Practical realization of autonomous multi-vehicle control in feature rich GPS-denied areas requires that agents possess the capability to generate and share maps with details adequate for successful operation in complex environments. In addition, coordination of behaviors among robotic vehicles in a swarm is greatly enhanced by exploitation of information acquired by all members of the group. The representation of, and mechanism for this information exchange and the control algorithms that utilize this information are the key components of autonomous multi-vehicle control. The use of stigmergic potential fields (SPF) and distributed control algorithms to realize adaptive, cooperative behaviors within the context of feature-rich GPS-denied environments is the focus of this paper. Specifically, the described control algorithms have been combined with the leading simultaneous localization and mapping (SLAM) approaches to yield robust multi-robot behaviors including exploration and mapping