Distributed inference-based multi-robot exploration
Distributed inference-based multi-robot exploration
复制标题
基于分布式推理的多机器人探索
DOI：
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发表时间：
2018
影响因子：
3.5
通讯作者：
Geoffrey A. Hollinger
中科院分区：
文献类型：
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作者：
Andrew J. Smith;Geoffrey A. Hollinger
这项工作提出了一种分布式多机器人探索技术，利用新的地图推理方法。推理技术使用观察到的地图结构来推断未观察到的地图特征。然后，团队协调探索地图的推断部分和观察部分。单个机器人通过考虑预期的信息增益和旅行成本来选择探索姿势。争端是通过预期旅行费用的当地拍卖来解决的。在模拟探索和硬件试验中证明了推理知情探索的好处。所提出的技术进行比较，对前沿和基于信息的探索方法与不同数量的代理和通信强度。地图推理使用公开的传感器数据集进行评估。所提出的推理技术提高了正确估计的环境子集的平均34.47%（最大108.28%），平均准确度为95.1%。这导致在所进行的试验中累积勘探路径长度减少13.15%。
This work proposes a technique for distributed multi-robot exploration that leverages novel methods of map inference. The inference technique uses observed map structure to infer unobserved map features. The team then coordinates to explore both the inferred and observed portions of the map. Individual robots select exploration poses by accounting for expected information gain and travel costs. Disputes are settled using local auctions of expected travel costs. The benefits of inference-informed exploration are demonstrated in both simulated explorations and hardware trials. The proposed technique is compared against frontier and information-based exploration approaches with varying numbers of agents and communication strengths. Map inference is evaluated using publicly available sensor datasets. The proposed inference technique improves the correctly estimated subset of the environment by an average of 34.47% (maximum 108.28%) with a mean accuracy of 95.1%. This leads to a 13.15% reduction in the cumulative exploration path length in the trials conducted.
影响因子：
3.5
作者：
Hornung, Armin;Wurm, Kai M.;Burgard, Wolfram
通讯作者：
Burgard, Wolfram