论文标题
通过多模式感应
Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing
论文作者
论文摘要
具有多模式传感（AIPPMS）的自适应信息路径计划（AIPPMS）考虑了配备多个传感器的代理的问题，每个传感器都具有不同的感应精度和能量成本。代理商的目标是探索环境并在未知的，部分可观察到的环境中受到其资源约束的信息。以前的工作集中在不太一般的适应性信息路径计划（AIPP）问题上，该问题仅考虑了代理人运动对收到的观察结果的影响。 AIPPMS问题通过要求代理的原因共同出现感应和移动的影响，同时平衡资源约束与信息目标，从而增加了额外的复杂性。我们将AIPPMS问题作为一种信念Markov决策过程，并具有高斯流程信念，并使用在线计划中使用连续的贝叶斯优化方法来解决它。我们的方法始终优于以前的AIPPMS解决方案，它几乎在每个实验中获得的平均奖励增加了一倍以上，同时还将环境中的根平方错误减少了50％。我们完全开放我们的实施方式，以帮助进一步发展和比较。
Adaptive Informative Path Planning with Multimodal Sensing (AIPPMS) considers the problem of an agent equipped with multiple sensors, each with different sensing accuracy and energy costs. The agent's goal is to explore the environment and gather information subject to its resource constraints in unknown, partially observable environments. Previous work has focused on the less general Adaptive Informative Path Planning (AIPP) problem, which considers only the effect of the agent's movement on received observations. The AIPPMS problem adds additional complexity by requiring that the agent reasons jointly about the effects of sensing and movement while balancing resource constraints with information objectives. We formulate the AIPPMS problem as a belief Markov decision process with Gaussian process beliefs and solve it using a sequential Bayesian optimization approach with online planning. Our approach consistently outperforms previous AIPPMS solutions by more than doubling the average reward received in almost every experiment while also reducing the root-mean-square error in the environment belief by 50%. We completely open-source our implementation to aid in further development and comparison.
