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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Information-Based Hierarchical Planning for a Mobile Sensing Network in Environmental Mapping
AU - Li, T.
AU - Tong, K.
AU - Xia, M.
AU - Li, B.
AU - De Silva, C.W.
N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - This article investigates the problem of information-based sampling design and path planning for a mobile sensing network to predict scalar fields of monitored environments. A hierarchical framework with a built-in Gaussian Markov random field model is proposed to provide adaptive sampling for efficient field reconstruction. In the proposed framework, a nonmyopic planner is operated at a sink to navigate the mobile sensing agents in the field to the sites that are most informative. Meanwhile, a myopic planner is carried out on board each agent. A tradeoff between computationally intensive global optimization and efficient local greedy search is incorporated into the system. The mobile sensing agents can be scheduled online through an anytime algorithm to visit and observe the high-information sites. Experiments on both synthetic and real-world datasets are used to demonstrate the feasibility and efficiency of the proposed planner in model exploitation and adaptive sampling for environmental field mapping.
AB - This article investigates the problem of information-based sampling design and path planning for a mobile sensing network to predict scalar fields of monitored environments. A hierarchical framework with a built-in Gaussian Markov random field model is proposed to provide adaptive sampling for efficient field reconstruction. In the proposed framework, a nonmyopic planner is operated at a sink to navigate the mobile sensing agents in the field to the sites that are most informative. Meanwhile, a myopic planner is carried out on board each agent. A tradeoff between computationally intensive global optimization and efficient local greedy search is incorporated into the system. The mobile sensing agents can be scheduled online through an anytime algorithm to visit and observe the high-information sites. Experiments on both synthetic and real-world datasets are used to demonstrate the feasibility and efficiency of the proposed planner in model exploitation and adaptive sampling for environmental field mapping.
KW - Adaptive sampling
KW - environmental field mapping
KW - Gaussian Markov random fields (GMRFs)
KW - information-driven planning
KW - mobile sensing networks (MSNs)
KW - Global optimization
KW - Image segmentation
KW - Mapping
KW - Markov processes
KW - Motion planning
KW - Any-time algorithms
KW - Environmental fields
KW - Environmental mapping
KW - Field reconstruction
KW - Gaussian markov random field models
KW - Hierarchical planning
KW - Mobile sensing agents
KW - Mobile sensing networks
KW - Mobile agents
U2 - 10.1109/JSYST.2019.2939250
DO - 10.1109/JSYST.2019.2939250
M3 - Journal article
VL - 14
SP - 1692
EP - 1703
JO - IEEE Systems Journal
JF - IEEE Systems Journal
SN - 1932-8184
IS - 2
M1 - 8844270
ER -