Plan-Based Policy-Learning for Autonomous Feature Tracking
Plan-Based Policy-Learning for Autonomous Feature Tracking
复制标题
用于自主特征跟踪的基于计划的策略学习
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
D. Magazzeni
中科院分区：
文献类型：
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作者：
M. Fox;D. Long;D. Magazzeni
绘制和跟踪有害藻类水华等生物海洋特征是环境科学中的一个重要问题。这个问题表现出高度的不确定性，这既是因为海洋的动态环境，也是因为遥感方面的挑战。基于计划的政策学习已被证明是一种强大的技术，可以在面对不确定性的情况下获得稳健的智能行为。在本文中，我们将这一技术应用于模拟，跟踪二维生物特征的外边缘，如有害藻华的表面。我们证明了基于计划的策略学习在模拟中导致了高精度的跟踪，即使在控制补丁形状的不确定性不能直接建模的情况下也是如此。我们给出了仿真结果，证明了该方法在实践中是可行的。我们现在正在与MBARI的海洋科学家合作，在海上进行物理测试。
Mapping and tracking biological ocean features, such as harmful algal blooms, is an important problem in the environmental sciences. The problem exhibits a high degree of uncertainty, because of both the dynamic ocean context and the challenges of sensing. Plan-based policy learning has been shown to be a powerful technique for obtaining robust intelligent behaviour in the face of uncertainty. In this paper we apply this technique in simulation, to the problem of tracking the outer edge of 2D biological features, such as the surfaces of harmful algal blooms. We show that plan-based policy-learning leads to highly accurate tracking in simulation, even in situations where the uncertainty governing the shape of the patch cannot be directly modelled. We present simulation results that give confidence that the approach could work in practice. We are now collaborating with ocean scientists at MBARI to perform physical tests at sea.