Physical sampling of water for off-site analysis is necessary for many applications like monitoring the quality of drinking water in reservoirs, understanding marine ecosystems, and measuring contamination levels in fresh-water systems. In this paper, the focus is on algorithms for efficient measurement and sampling using a multi-robot, data-driven, water-sampling behavior, where autonomous surface vehicles plan and execute water sampling using the chlorophyll density as a cue for plankton-rich water samples. We use two Autonomous Surface Vehicles (ASVs), one equipped with a water quality sensor and the other equipped with a water-sampling apparatus. The ASV with the sensor acts as an explorer, measuring and building a spatial map of chlorophyll density in the given region of interest. The ASV equipped with the water sampling apparatus makes decisions in real time on where to sample the water based on the suggestions made by the explorer robot. We evaluate the system in the context of measuring chlorophyll distributions. We do this both in simulation based on real geophysical data from MODIS measurements, and on real robots in a water reservoir. We demonstrate the effectiveness of the proposed approach in several ways including in terms of mean error in the interpolated data as a function of distance traveled.
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Collaborative Sampling Using Heterogeneous Marine Robots Driven by Visual Cues
This paper addresses distributed data sampling in marine environments using robotic devices. We present a method to strategically sample locally observable features using two classes of sensor platforms. Our system consists of a sophisticated autonomous surface vehicle (ASV) which strategically samples based on information provided by a team of inexpensive sensor nodes. The sensor nodes effectively extend the observational capabilities of the vehicle by capturing georeferenced samples from disparate and moving points across the region. The ASV uses this information, along with its own observations, to plan a path so as to sample points which it expects to be particularly informative. We compare our approach to a traditional exhaustive survey approach and show that we are able to effectively represent a region with less energy expenditure. We validate our approach through simulations and test the system on real robots in field.
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- Award ID(s):
- 1637876
- NSF-PAR ID:
- 10127558
- Date Published:
- Journal Name:
- Conference on Computer and Robot Vision (CRV)
- Page Range / eLocation ID:
- 87 to 94
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Understanding how ecology and phylogeny shape parasite communities can inform parasite control and wildlife conservation initiatives while contributing to the study of host species evolution.
We tested the relative strengths of phylogeny and ecology in driving parasite community structure in a host whose ecology diverges significantly from that of its closest phylogenetic relatives.
We characterized the gastrointestinal (GI) parasite community of wild geladas
Theropithecus gelada , primates that are closely related to baboons but specialized to graminovory in the Ethiopian Highlands.Geladas exhibited very constrained GI parasite communities: only two genera (
Oesophagostomum andTrichostrongylus ) were identified across 305 samples. This is far below the diversity reported for baboons (Papio spp.) and at the low end of the range of domestic grazers (e.g.Bos taurus ,Ovis aries ) inhabiting the same region and ecological niche.Using deep amplicon sequencing, we identified 15 amplicon sequence variants (ASVs) within the two genera, seven of which matched to
Oesophagostomum sp., seven toTrichostrongylus sp., and one toT. vitrinus .Population was an important predictor of ASV richness. Geladas in the most ecologically disturbed area of the national park exhibited approximately four times higher ASV richness than geladas at a less disturbed location within the park.
In this system, ecology was a stronger predictor of parasite community structure than was phylogeny, with geladas sharing more elements of their parasite communities with other grazers in the same area than with closely related sister taxa.
A free
Plain Language Summary can be found within the Supporting Information of this article. -
With recent technological advances in sensor nodes, IoT enabled applications have great potential in many domains. However, sensing data may be inaccurate due to not only faults or failures in the sensor and network but also the limited resources and transmission capability available in sensor nodes. In this paper, we first model streams of IoT data as a handful of sampled data in the transformed domain while assuming the information attained by those sampled data reveal different sparsity profiles between normal and abnormal. We then present a novel approach called AD2 (Anomaly Detection using Approximated Data) that applies a transformation on the original data, samples top k-dominant components, and detects data anomalies based on the disparity in k values. To demonstrate the effectiveness of AD2 , we use IoT datasets (temperature, humidity, and CO) collected from real-world wireless sensor nodes. Our experimental evaluation demonstrates that AD2 can approximate and successfully detect 64%-94% of anomalies using only 1.9% of the original data and minimize false positive rates, which would otherwise require the entire dataset to achieve the same level of accuracy.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CRV.2017.49
