A Successive-Elimination Approach to Adaptive Robotic Sensing
A Successive-Elimination Approach to Adaptive Robotic Sensing
复制标题
自适应机器人传感的逐次消除方法
DOI：
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发表时间：
2018
期刊：
影响因子：
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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
我们研究了一个自适应源寻找问题，其中移动机器人必须识别具有背景发射的环境中最强的信号发射器。背景信号可能高度异构，并且可能误导基于后退地平线控制的算法。我们提出了 AdaSearch，一种面对异构背景噪声时自适应源搜索的通用算法。 AdaSearch 将全局轨迹规划与原则置信区间相结合，以便将测量集中在有前景的区域，同时保证整个区域的充分覆盖。理论分析表明，当背景信号的分布变化很大时，AdaSearch 比均匀采样策略更有优势。仿真实验表明，当应用于放射源寻找问题时，AdaSearch 的性能优于均匀采样和基于当前文献的后退时间范围信息最大化方法。我们还在硬件中演示了 AdaSearch，进一步证明了其实时实施的潜力。
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 AdaSearch, a general algorithm for adaptive source seeking in the face of heterogeneous background noise. AdaSearch 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 AdaSearch 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, AdaSearch outperforms both uniform sampling and a receding time horizon information-maximization approach based on the current literature. We also demonstrate AdaSearch in hardware, providing further evidence of its potential for real-time implementation.