A Data-Driven Deployment Approach for Persistent Monitoring in Aquatic Environments

Alam, Tauhidul; Reis, Gregory Murad; Bobadilla, Leonardo; Smith, Ryan N.

doi:10.1109/irc.2018.00030

Cited by 23 publications

(18 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our previous work [35] develops a control policy using a Markov decision process (MDP) on the fully observable states of the predicted ocean current model. We can combine this localization method with the previous control method by introducing a partially observable Markov decision process (POMDP) framework.…”

Section: Discussionmentioning

confidence: 99%

“…However, these methods are computationally expensive and require a large amount of memory capacity. Also, a minimally-actuated and resource-constrained underwater vehicle like a drifter which is designed for long-term deployment [5], cannot avail the requirements of these Bayesian filters based methods. In [12], [18], the velocity analysis of spatiotemporally varying water current correlating with the velocity of the vehicle is incorporated to limit error growth in the position estimation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Underactuated Vehicle Localization Method in Marine Environments

Alam

Reis

Bobadilla

et al. 2018

OCEANS 2018 MTS/IEEE Charleston

Self Cite

View full text Add to dashboard Cite

The underactuated vehicles are apposite for the longterm deployment and data collection in spatiotemporally varying marine environments. However, these vehicles need to estimate their positions (states) with intrinsic sensing in their long-term trajectories. In previous studies, autonomous underwater vehicles have commonly used vision and range sensors for autonomous state estimation. Inspired by the intrinsic sensing and the persistent deployment, we investigate the localization problem (state estimation) for an inexpensive and underactuated drifting vehicle called a drifter. In this paper, we present a localization method for the drifter making use of the observations of a proprioceptive sensor, i.e., compass. We create the water flow pattern within a given region from ocean model predictions, develop a stochastic motion model, and analyze the persistent water flow behavior. Given a distribution of initial deployment states of the drifter at a particular depth of the water column within the region and the water flow pattern, our method finds attractors and their transient groups at the given depth as the persistent behavior of the water flow. A most-likely localized trajectory of the drifter for a sequence of compass observations is generated based on the persistent behavior of the water flow and hidden Markov model. Our simulation results based on data from ocean model predictions substantiate good performance of our proposed localization method with a low error rate of the state estimation in the long-term trajectory of the drifter.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Underactuated Vehicle Localization Method in Marine Environments

Alam

Reis

Bobadilla

et al. 2018

OCEANS 2018 MTS/IEEE Charleston

Self Cite

View full text Add to dashboard Cite

show abstract

“…13 To observe under water, where satellites cannot observe, systems, such as those involving buoys with sensors and cameras and underwater vehicles, have been proposed. [14][15][16][17][18] Argo Float 19 is one such system consisting of many buoys scattered throughout the world's oceans to collect salinity and water temperature moving from sea surface to a depth of around 1000 m. Compared to a buoys-based system, autonomous vehicles must provide spatially unrestricted ocean exploration. Actually, Thompson pointed out that future marine monitoring systems would rely heavily on autonomous vehicles to enable persistent and heterogeneous measurements needed to understand the ocean's impact on the climate system.…”

Section: Related Workmentioning

confidence: 99%

Underwater and airborne monitoring of marine ecosystems and debris

Watanabé

Shao

Miura

2019

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Advancing the sustainable use and conservation of marine environments is urgent. Tons of debris including macro-and microplastics generated on land are entering the oceans, marine resources are decreasing, and many species are facing extinction. Though satellite remote sensing techniques are commonly used for global environmental monitoring, it is still difficult to detect small objects such as floating debris on the vast ocean surface, and the ecosystems deep in the oceans where light does not reach are unobservable. An autonomous monitoring system consisting of optimally controlled robots is required for acquiring spatiotemporally rich marine data. However, object detection in marine environments, which is a necessary function the robots should have for underwater and aerial monitoring, has not been extensively studied. Here, we argue that state-of-the-art deep-learning-based object detection works well for monitoring underwater ecosystems and marine debris. We found that by using the deep-learning object-detection algorithm YOLO v3, underwater sea life and debris floating on the ocean surface can be detected with mean average precision of 69.6% and 77.2%, respectively. We anticipate our results to be a starting point for developing tools for enabling safe and precise acquisition of marine data to elucidate and utilize this last frontier. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

“…Several efforts (Alam, Reis, Bobadilla, & Smith, ; Das et al., ; Hollinger and Sukhatme, ; Lawrance, Chung, & Hollinger, ; Ma, Liu, Heidarsson, & Sukhatme, ; Yoo et al., ) use an information quality metric (e.g., information gain or variance reduction) along with system constraints (e.g., battery) to plan motion during environmental monitoring in aqueous environments, mostly with AUVs and ASVs. These works plan a path and choose a waypoint (or depth) based on a data‐driven metric, whereas our work assumes the target depth has already been chosen.…”

Section: Related Workmentioning

confidence: 99%

Sensing water properties at precise depths from the air

Ore

Detweiler

2018

Journal of Field Robotics

View full text Add to dashboard Cite

Water properties are critical to our understanding and managing of freshwater systems that change rapidly with depth. This work presents an unmanned aerial vehicle (UAV) -based method of keeping a submerged, cable-suspended sensor payload at a precise depth, with 95% of sensor readings within ±8.4 cm of the target depth. We use a depth altimeter attached at the terminus of a 3.5 m semirigid cable as the sole input to a depth controller actuated by the UAV's motors.First, we simulate the UAV-cable-payload system with disturbances of wind, water, signal delay, and GPS drift and then use parameters discovered during simulation to guide implementation. We characterize the depth altimeter during translation and find that 95% of the readings are within ±7 mm of ground truth, with a steady-state error of ±3 mm. In field experiments, we compare the depth precision of our new method to previous methods that used the UAV's altitude as a proxy for submerged sensor depth, specifically 1) only using the UAV's air pressure altimeter; and 2) fusing UAV-mounted ultrasonic sensors with the air pressure altimeter. Our new method reduces the standard deviation of depth readings from 16.1 to 4.2 cm in winds up to 8.0 ms −1 . We show the step response of the depth-altimeter method when transitioning between target depths. Finally, we explore a longer, 8.0 m cable and show that our depth controller still outperforms air altimeter and ultrasonic methods and allows scientists to increase the spatiotemporal resolution of water property datasets.

show abstract

A Data-Driven Deployment Approach for Persistent Monitoring in Aquatic Environments

Cited by 23 publications

References 23 publications

An Underactuated Vehicle Localization Method in Marine Environments

An Underactuated Vehicle Localization Method in Marine Environments

Underwater and airborne monitoring of marine ecosystems and debris

Sensing water properties at precise depths from the air

Contact Info

Product

Resources

About