Adaptive path planning for depth‐constrained bathymetric mapping with an autonomous surface vessel

Wilson, Troy; Williams, Stefan B.

doi:10.1002/rob.21718

Cited by 21 publications

(10 citation statements)

References 40 publications

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“…This section presents the simulation and field tests by implementing the proposed method in field estimation and mapping. The proposed approach is also compared with the existing methods for coverage sampling design and path planning, including square grid-based spanning tree coverage (SGSTC) planner [23], discrete monotone polygonal partitioning-based (DMPP) planner [16], and hexagonal gridbased TSP (HGTSP) planner. The SGSTC method was originally designed for sweep coverage planning, which is adopted to coverage sampling for comparison in the experiment.…”

Section: Resultsmentioning

confidence: 99%

“…In contract, many other planners try to generate a coverage path first and then plot the samples along the path. In the literature, cellular decomposition methods, such as Boustrophedon path [13], lawnmower path [14], and their variants [15], [16], have been studied to generate a transect survey pattern consisting of a series of parallel linear transects to cover the study regions. Then data samples are taken while traveling back and forth along the generated coverage path.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Coverage Sampling Planner for UAV-enabled Environmental Exploration and Field Mapping

Teng

Wang

Q.-H.

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Unmanned Aerial Vehicles (UAVs) have been implemented for environmental monitoring by using their capabilities of mobile sensing, autonomous navigation, and remote operation. However, in real-world applications, the limitations of on-board resources (e.g., power supply) of UAVs will constrain the coverage of the monitored area and the number of the acquired samples, which will hinder the performance of field estimation and mapping. Therefore, the issue of constrained resources calls for an efficient sampling planner to schedule UAV-based sensing tasks in environmental monitoring. This paper presents a mission planner of coverage sampling and path planning for a UAV-enabled mobile sensor to effectively explore and map an unknown environment that is modeled as a random field. The proposed planner can generate a coverage path with an optimal coverage density for exploratory sampling, and the associated energy cost is subjected to a power supply constraint. The performance of the developed framework is evaluated and compared with the existing state-of-the-art algorithms, using a real-world dataset that is collected from an environmental monitoring program as well as physical field experiments. The experimental results illustrate the reliability and accuracy of the presented coverage sampling planner in a prior survey for environmental exploration and field mapping.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Coverage Sampling Planner for UAV-enabled Environmental Exploration and Field Mapping

Teng

Wang

Q.-H.

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Continued monitoring of these parameters is a labor-intensive task, thus, in the future works, we would like to investigate mechanisms to reduce the reliance on such parameters. It would also be of interest to validate our generalized ORangE on other robot platforms, e.g., marine robots that are tasked with monitoring the marine ecology as shown in the works like [4,15].…”

Section: Discussionmentioning

confidence: 99%

ORangE: Operational Range Estimation for Mobile Robot Exploration on a Single Discharge Cycle

Tiwari¹,

Xiao²,

Kyrki³

et al. 2019

Preprint

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This paper presents an approach for estimating the operational range for mobile robot exploration on a single battery discharge. Deploying robots in the wild usually requires uninterrupted energy sources to maintain the robot's mobility throughout the entire mission. However, due to unknown nature of the environments, recharging is usually not an option, due to the lack of pre-installed recharging stations or other mission constraints. In these cases, the ability to model the on-board energy consumption and estimate the operational range is crucial to prevent running out of battery in the wild. To this end, this work describes our recent findings that quantitatively break down the robot's on-board energy consumption and predict the operational range to guarantee safe mission completion on a single battery discharge cycle. Two range estimators with different levels of generality and model fidelity are presented, whose performances were validated on physical robot platforms in both indoor and outdoor environments. Model performance metrics are also presented as benchmarks.

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“…However, ASV surveys may still be superior to topobathymetric LiDAR where conditions are especially turbid, airspace is highly regulated, and/or weather conditions restrict UAV and airborne LiDAR acquisition. Where depths are constrained for even ASV surveys, new Gaussian Process (GP) algorithms can be implemented to constrain survey missions with bounding polygons and create efficient tracks [48]. Certainly, environmental limitations of ASV operations have been greatly overcome by technological advances, and small ASV platforms are increasingly being deployed in estuaries for a variety of scientific and applied purposes.…”

Section: Small Autonomous Surface Vessels (Sasvs)mentioning

confidence: 99%

Advancing Estuarine Shoreline Change Analysis Using Small Uncrewed Autonomous Systems

R. Allen,

Eulie,

Polk

et al. 2023

Estuary Research - Recent Advances [Working Title]

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Estuarine shorelines face the threats of accelerating sea-level rise, recurrent storms, and disruptions of natural sediment and ecological adjustments owing to historic human interventions. The growing availability and technical capability of uncrewed systems (UxS), including remote or autonomous aerial and surface vessels, provide new opportunities to study and understand estuarine shoreline changes. This chapter assesses the state of the technology, interdisciplinary science and engineering literature, and presents case studies from the Chesapeake Bay, Virginia, and coastal North Carolina, USA, that demonstrate new insights into coastal geomorphic processes and applications to managing complex and dynamic estuarine shorelines. These technologies enhance the collection of geospatial environmental data, coastal monitoring, reduce spatial uncertainty, and support measurement of alongshore and onshore/offshore sediment fluxes. Case studies in this chapter highlight scientific insights such as shoreline responses to sea-level rise as well as the practical value of these technologies to develop adaptive management solutions such as living shorelines and nature-based features.

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Adaptive path planning for depth‐constrained bathymetric mapping with an autonomous surface vessel

Cited by 21 publications

References 40 publications

Coverage Sampling Planner for UAV-enabled Environmental Exploration and Field Mapping

Coverage Sampling Planner for UAV-enabled Environmental Exploration and Field Mapping

ORangE: Operational Range Estimation for Mobile Robot Exploration on a Single Discharge Cycle

Advancing Estuarine Shoreline Change Analysis Using Small Uncrewed Autonomous Systems

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