Riverine Coverage with an Autonomous Surface Vehicle over Known Environments

Karapetyan, Nare; Braude, Adam; Moulton, Jason; Burstein, Joshua A.; White, S. M.; O’Kane, Jason M.; Rekleitis, Ioannis

doi:10.1109/iros40897.2019.8968084

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…Therefore, these are the preferred methods whenever neither time-related restrictions nor expensive exploration costs are present. L-Cover, T-Cover and Z-cover [6] are some common methods and they fall into the lawnmower category. These methods are extremely useful in situations where the work or search space is very narrow, such as with rivers and streams; however, they have lesser impact observational-wise as the water body widens due to energy availability concerns.…”

Section: Related Workmentioning

confidence: 99%

A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study

Peralta¹,

Reina²,

Toral³

et al. 2021

IEEE Access

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Bayesian Optimization is a sequential method for obtaining the maximum of an unknown function that has gained much popularity in recent years. Bayesian Optimization is commonly used to monitor the surface of large-scale aquatic environments using an Autonomous Surface Vehicle. We propose to model water quality parameters using Gaussian Processes, and propose three different adaptations of classical Acquisition Functions in order to explore an unknown space, considering surface vehicle restrictions. The proposed Sequential Bayesian Optimization system uses the aforementioned information in order to monitor the Lake and also to obtain a water quality model, which has an associated uncertainty map. For evaluation, the Mean Squared Error of the resulting approximated models are compared. Afterwards, they are compared with other monitoring algorithms, like the Traveling Salesman Problem, using Genetic Algorithms and Lawnmower. Concluding remarks indicate that the proposed method not only performs better while minimizing the Mean Squared Error (via active monitoring), but also manages to quickly identify an approximate of the black-box function, which is very useful for monitoring lakes like Ypacarai Lake (60 km 2 ) in Paraguay. Additionally, the proposed method reduces the MSE by 25% when compared with Traveling Salesman Problem-based monitoring algorithms and also provides a more robust solution, i.e., 30% more independent of initial conditions, when compared with known robust coverage methods like the lawnmower method.

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Section: Related Workmentioning

confidence: 99%

A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study

Peralta¹,

Reina²,

Toral³

et al. 2021

IEEE Access

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show abstract

“…The second approach splits the area between robots by the size of the coverage region and the distance from the starting point, and then finds for each robot the single optimal path. In addition we have developed different coverage patterns designed for river surveying operations [15], [16]. Depending on the type of the sensors and the aim of the exploration different patterns can be deployed.…”

Section: Proposed Approachmentioning

confidence: 99%

Dynamic Autonomous Surface Vehicle Control and Applications in Environmental Monitoring

Karapetyan

Moulton

Rekleitis

2019

Oceans 2019 MTS/Ieee Seattle

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This paper addresses the problem of robotic operations in the presence of adversarial forces. We presents a complete framework for survey operations: waypoint generation, modelling of forces and tuning the control. In many applications of environmental monitoring, search and exploration, and bathymetric mapping, the vehicle has to traverse in straight lines parallel to each other, ensuring there are no gaps and no redundant coverage. During operations with an Autonomous Surface Vehicle (ASV) however, the presence of wind and/or currents produces external forces acting on the vehicle which quite often divert it from its intended path. Similar issues have been encountered during aerial or underwater operations. By measuring these phenomena, wind and current, and modelling their impact on the vessel, actions can be taken to alleviate their effect and ensure the correct trajectory is followed.

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“…If a long-range map of the external forces is available then large scale planning can take the effects of the external forces into account. For example, coverage planning algorithms [6,5], can include the force map as an input variable in order to improve mission planning. In a smaller scale, real-time force measurements can be used in a reactive controller to accurately track the desired trajectory.…”

Section: Problem Definitionmentioning

confidence: 99%

Dynamic Autonomous Surface Vehicle Controls Under Changing Environmental Forces

Moulton¹,

Karapetyan²,

Kalaitzakis³

et al. 2019

Preprint

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The ability to navigate, search, and monitor dynamic marine environments such as ports, deltas, tributaries, and rivers presents several challenges to both human operated and autonomously operated surface vehicles. Human data collection and monitoring is overly taxing and inconsistent when faced with large coverage areas, disturbed environments, and potentially uninhabitable situations. In contrast, the same missions become achievable with Autonomous Surface Vehicles (ASVs) configured and capable of accurately maneuvering in such environments. The two dynamic factors that present formidable challenges to completing precise maneuvers in coastal and moving waters are currents and winds. In this work, we present novel and inexpensive methods for sensing these external forces, together with methods for accurately controlling an ASV in the presence of such external forces. The resulting platform is capable of deploying bathymetric and water quality monitoring sensors. Experimental results in local lakes and rivers demonstrate the feasibility of the proposed approach.

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Riverine Coverage with an Autonomous Surface Vehicle over Known Environments

Cited by 14 publications

References 14 publications

A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study

A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study

Dynamic Autonomous Surface Vehicle Control and Applications in Environmental Monitoring

Dynamic Autonomous Surface Vehicle Controls Under Changing Environmental Forces

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