Exploring the Tradeoffs Between Systematic and Random Exploration in Mobile Sensors
Exploring the Tradeoffs Between Systematic and Random Exploration in Mobile Sensors
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探索移动传感器系统探索和随机探索之间的权衡
DOI：
10.1145/3616388.3617524
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Bölöni, Ladislau
中科院分区：
文献类型：
--
作者：
Matloob, Samuel;Dutta, Ayan;Kreidl, Patrick;Turgut, Damla;Bölöni, Ladislau
移动的传感器的移动对从感兴趣区域收集的信息以及模型可以在任何时刻根据收集的信息建立的估计的质量具有关键影响。使传感器以规则模式移动的系统探测模型和随机移动模型都具有特定的优点。关于这两个极端之间的模型的研究较少。在本文中，我们提出了网格有限的随机性（GLR），一个家庭的路径规划算法的基础上采样航路点从一个特定的分辨率的网格。我们提出了三种不同的顺序，其中移动的传感器访问这些航点：新的样本添加到路径的末端（GLR-EOP），最小的绕道（GLR-SD），和最短的路径近似Christofides的算法。在Waterberry Farms基准中进行的广泛模拟研究表明，GLR变化提供了一些好处，在特定情况下，使其优于完全随机和完全系统化的勘探路径。
The movement of a mobile sensor has a critical impact on the information gathered from the area of interest, as well as the quality of the estimate that a model can build from the collected information at any moment in time. Both systematic exploration models, which make the sensor move in regular patterns, and random movement models have specific advantages. There is less research concerning models that are positioned between these two extremes. In this paper, we propose Grid Limited Randomness (GLR), a family of path planning algorithms based on sampling waypoints from a grid of a specific resolution. We propose three variations differentiated by the order in which the mobile sensor visits these waypoints: new samples added to the end of the path (GLR-EOP), smallest detour (GLR-SD), and the shortest path as approximated by Christofides' algorithm. An extensive simulation study in the Waterberry Farms benchmark shows that the GLR variations offer benefits that, in specific circumstances, make them preferable to both fully random and fully systematic exploration paths.
使用深度平均场强化学习的多机器人信息采样
DOI：
10.1109/smc52423.2021.9658795
发表时间：
2021
期刊：
and Cybernetics (SMC
影响因子：
--
作者：
Said, Tuffa;Wolbert, Jeffery;Khodadadeh, Siavash;Dutta, Ayan;Kreidl, O. Patrick;Boloni, Ladislau;Roy, Swapnoneel
通讯作者：
Roy, Swapnoneel
基于非平稳高斯过程的环境现象的多机器人主动感知
DOI：
--
发表时间：
2014
期刊：
Adaptive Agents and Multi-Agent Systems
影响因子：
--
作者：
Ruofei Ouyang;K. H. Low;Jie Chen;Patrick Jaillet
通讯作者：
Patrick Jaillet
影响因子：
1.3
作者：
Chekuri, Chandra;Korula, Nitish;Pal, Martin
通讯作者：
Pal, Martin