A Successive-Elimination Approach to Adaptive Robotic Source Seeking
A Successive-Elimination Approach to Adaptive Robotic Source Seeking
复制标题
自适应机器人寻源的逐次消除方法
DOI：
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复制
发表时间：
2018
影响因子：
7.8
通讯作者：
C. Tomlin
中科院分区：
文献类型：
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作者：
Esther Rolf;David Fridovich;Max Simchowitz;B. Recht;C. Tomlin
在这篇文章中，我们研究了一个自适应源搜索问题，其中一个移动的机器人必须识别最强的发射器（S）的信号在环境中的背景辐射。背景信号可能是高度异质的，并且可能误导基于滚动时域控制的算法。我们提出了<inline-formula><tex-math notation="LaTeX">$mathtt {AdaSearch}$</tex-math></inline-formula>，这是一种针对异构背景噪声的自适应源搜索通用算法。<inline-formula><tex-math notation="LaTeX">$mathtt {AdaSearch}$</tex-math></inline-formula>将全局轨迹规划与原则性置信区间相结合，以便将测量集中在有希望的区域，同时保证对整个区域的充分覆盖。理论分析表明，当背景信号的分布高度可变时，<inline-formula><tex-math notation="LaTeX">$mathtt {AdaSearch}$</tex-math></inline-formula>比均匀采样策略具有更好的性能。仿真实验表明，<inline-formula><tex-math notation="LaTeX">$mathtt {AdaSearch}$</tex-math></inline-formula>应用于放射性源搜索问题时，其性能优于均匀采样和基于当前文献的后退时域信息最大化方法。我们还展示了<inline-formula><tex-math notation="LaTeX">$mathtt {AdaSearch}$</tex-math></inline-formula>在硬件中，提供了进一步的证据，其潜力的实时实现。
In this article, we study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background signals may be highly heterogeneous and can mislead algorithms that are based on receding horizon control. We propose <inline-formula><tex-math notation="LaTeX">$mathtt {AdaSearch}$</tex-math></inline-formula>, a general algorithm for adaptive source seeking in the face of heterogeneous background noise. <inline-formula><tex-math notation="LaTeX">$mathtt {AdaSearch}$</tex-math></inline-formula> combines global trajectory planning with principled confidence intervals in order to concentrate measurements in promising regions while guaranteeing sufficient coverage of the entire area. Theoretical analysis shows that <inline-formula><tex-math notation="LaTeX">$mathtt {AdaSearch}$</tex-math></inline-formula> confers gains over a uniform sampling strategy when the distribution of background signals is highly variable. Simulation experiments demonstrate that when applied to the problem of radioactive source-seeking, <inline-formula><tex-math notation="LaTeX">$mathtt {AdaSearch}$</tex-math></inline-formula> outperforms both uniform sampling and a receding time horizon information-maximization approach based on the current literature. We also demonstrate <inline-formula><tex-math notation="LaTeX">$mathtt {AdaSearch}$</tex-math></inline-formula> in hardware, providing further evidence of its potential for real-time implementation.