2022
DOI: 10.1007/s10514-022-10048-7
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Scalable multirobot planning for informed spatial sampling

Abstract: Persistent monitoring of a spatiotemporal fluid process requires data sampling and predictive modeling of the process being monitored. In this paper we present PASST algorithm: Predictive-model based Adaptive Sampling of a Spatio-Temporal process. PASST is an adaptive robotic sampling algorithm that leverages predictive models to efficiently and persistently monitor a fluid process in a given region of interest. Our algorithm makes use of the predictions from a learned prediction model to plan a path for an au… Show more

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Cited by 9 publications
(4 citation statements)
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“…Burks et al (2019) develop a Gaussian mixture model, and develops a clustering-based technique for mixture condensation that scales well to large belief space. Manjanna et al (2022) leverage a multi-resolution aggregation in the feature space to solve the multirobot coverage problem as a MDP. Hoerger et al (2022) propose a multi-level POMDP solver that uses multiple levels of approximation to the system dynamics to reduce the complexity of the forward simulations used in Monte-Carlo Tree Search.…”
Section: Continuous-state Pomdpmentioning
confidence: 99%
“…Burks et al (2019) develop a Gaussian mixture model, and develops a clustering-based technique for mixture condensation that scales well to large belief space. Manjanna et al (2022) leverage a multi-resolution aggregation in the feature space to solve the multirobot coverage problem as a MDP. Hoerger et al (2022) propose a multi-level POMDP solver that uses multiple levels of approximation to the system dynamics to reduce the complexity of the forward simulations used in Monte-Carlo Tree Search.…”
Section: Continuous-state Pomdpmentioning
confidence: 99%
“…Several approaches allocate optimally a set of predetermined locations to the robotic team, usually based on information content and location [20]. Other approaches exploit prior knowledge of the phenomenon, to make informative decisions [21], or they rely on a large amount of data, often coming from cameras [22,23]. Moreover, global knowledge can be injected into the planning, in the form of attractor landmarks [22] or by exploiting an underlying model of the phenomenon [24].…”
Section: Multi-robot Systems For Gas Sensingmentioning
confidence: 99%
“…Other research focuses on mobile sensors, e.g., robots [25,7,15,8], hence the goal is to move them to the most informative sensing locations. Some work aims to adapt the sampling process to cope with spatial (and, often, temporal) phenomena, preserving some spatial properties of the WSN or the phenomenon under observation [9,12,26].…”
Section: Motivation and Related Workmentioning
confidence: 99%