Model-Based Adaptive Behavior Framework for Optimal Acoustic Communication and Sensing by Marine Robots
Model-Based Adaptive Behavior Framework for Optimal Acoustic Communication and Sensing by Marine Robots
复制标题
基于模型的自适应行为框架，用于海洋机器人的最佳声学通信和传感
DOI：
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发表时间：
2013
影响因子：
4.1
通讯作者：
H. Schmidt
中科院分区：
文献类型：
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作者：
Toby Schneider;H. Schmidt
在本文中，一个混合的数据和模型为基础的自主环境适应框架，它允许自主水下航行器（AUV）与声学传感器遵循的路径，优化他们的能力，以保持与声学接触的最佳传感或通信的连接。适应框架内实现的行为为基础的面向任务的操作套件间隔编程（MOOS-IvP）的海洋自治架构，并使用一个新的嵌入式高保真声学建模基础设施，通用机器人声学模型（MAM），提供实时估计的声学环境下不断变化的环境和情景场景。一组行为，结合联合收割机适应当前的声学环境的战略，扩展决策范围超出了典型的基于行为的系统已经开发，实施，并在一系列的现场实验和虚拟实验中演示MOOS-IvP模拟。
In this paper, a hybrid data- and model-based autonomous environmental adaptation framework is presented which allows autonomous underwater vehicles (AUVs) with acoustic sensors to follow a path which optimizes their ability to maintain connectivity with an acoustic contact for optimal sensing or communication. The adaptation framework is implemented within the behavior-based mission-oriented operating suite-interval programming (MOOS-IvP) marine autonomy architecture and uses a new embedded high-fidelity acoustic modeling infrastructure, the generic robotic acoustic model (GRAM), to provide real-time estimates of the acoustic environment under changing environmental and situational scenarios. A set of behaviors that combine adaptation to the current acoustic environment with strategies that extend the decision horizon beyond that of typical behavior-based systems have been developed, implemented, and demonstrated in a series of field experiments and virtual experiments in a MOOS-IvP simulation.