Data-driven comparison of spatio-temporal monitoring techniques

Caley, Jeffrey A.; Hollinger, Geoffrey A.

doi:10.23919/oceans.2015.7401988

Cited by 5 publications

(1 citation statement)

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“…Information Gathering (IG) algorithms guide such exploration using an information metric which represents the informativeness of the environment variable under study in particular locations, and this metric is used to drive the data recording process towards the more informative spots whilst minimizing a cost, such as the number of measurements, the navigation distance or the mission time. IG algorithms have been used for different types of exploration, for instance; (a) goal-based, where the objective is traveling from an initial location to a goal location with a given cost budget [8], [9], [10], (b) front-based, for traversing a given threshold area [11], [12], (c) frontier-based, usually for indoor environment mapping [13], [14], [15], [16], (d) multimodal, using different data sources [17], [18], (e) multirobot, using multiple robots [19], [20], [21], (f) hotspot-based, to find environmental variable hotspots [22], [23], and (g) coveragebased, for environmental variables dense estimation [24], [25], which is, in fact, the focus of this work.…”

Section: B Related Workmentioning

confidence: 99%

Adaptive Visual Information Gathering for Autonomous Exploration of Underwater Environments

2021

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This work presents the development and field testing of a novel adaptive visual information gathering (AVIG) framework for autonomous exploration of benthic environments using AUVs. The objective is to adapt dynamically the robot exploration using the visual information gathered online. This framework is based on a novel decision-time adaptive replanning (DAR) behavior that works together with a sparse Gaussian process (SGP) for environmental modeling and a Convolutional Neural Network (CNN) for semantic image segmentation. The framework is executed in mission time. The SGP uses semantic data obtained from stereo images to probabilistically model the spatial distribution of certain species of seagrass that colonize the sea bottom forming widespread meadows. The uncertainty of the probabilistic model provides a measure of sampling informativeness to the DAR behavior. The DAR behavior has been designed to execute successive informative paths, without stopping, considering the newest information obtained from the SGP. We solve the information path planning (IPP) problem by means of a novel depthfirst (DF) version of the Monte Carlo tree search (MCTS). The DF-MCTS method has been designed to explore the state-space in a depth-first fashion, provide solution paths of a given length in an anytime manner, and reward smooth paths for field realization with non-holonomic robots. The complete framework has been integrated in a ROS environment as a high level layer of the AUV software architecture. A set of simulations and field testing show the effectiveness of the framework to gather data in P. oceanica environments.

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