Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments

Xiong, Chengke; Zhou, Hexiong; Lu, Di; Zeng, Zheng; Lian, Lian; Yu, Caoyang

doi:10.3390/s20092515

Cited by 24 publications

(13 citation statements)

References 42 publications

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“…Their former work proved the GTSP solver GLNS performs better in terms of speed and path generation than other TSP solvers [17]. Our former studies [18][19][20][21][22][23][24] involved a lot of research on path planning in underwater and sea surface cases. Concerning the sea surface, Yuanchang Liu et al's work [25][26][27] presents several algorithms for single and multiple unmanned surface vehicles.…”

Section: Informative Path Planningmentioning

confidence: 96%

Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments

2021

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Hybrid aerial underwater vehicles (HAUV) are a new frontier for vehicles. They can operate both underwater and aerially, providing enormous potential for a wide range of scientific explorations. Informative path planning is essential to vehicle autonomy. However, covering an entire mission region is a challenge to HAUVs because of the possibility of a multidomain environment. This paper presents an informative trajectory planning framework for planning paths and generating trajectories for HAUVs performing multidomain missions in dynamic environments. We introduce the novel heuristic generalized extensive neighborhood search GLNS–k-means algorithm that uses k-means to cluster information into several sets; then through the heuristic GLNS algorithm, it searches the best path for visiting these points, subject to various constraints regarding path budgets and the motion capabilities of the HAUV. With this approach, the HAUV is capable of sampling and focusing on regions of interest. Our method provides a significantly more optimal trajectory (enabling collection of more information) than ant colony optimization (ACO) solutions. Moreover, we introduce an efficient online replanning scheme to adapt the trajectory according to the dynamic obstacles during the mission. The proposed replanning scheme based on KD tree enables significantly shorter computational times than the scapegoat tree methods.

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Section: Informative Path Planningmentioning

confidence: 96%

Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments

2021

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“…In contrast, sampling and evolutionary-based strategies, have been used for adaptive frameworks, and have yield high performance. For instance, [36], [23], [31], [37], [38] base their strategies in modified versions of standard sampling-based strategies [39], [40], considering the information gain. In particular, Hollinger et al [23] presented a rapidly-exploring IG tree (RIG-tree), that was the base of a further developed two-step planning process presented by Viseras et al [31].…”

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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“…This method has high autonomy and reliability but only considers the horizontal plane motion. Xiong et al (2020) discussed a rapidly exploring random tree star (RRT * ) method which is a variant algorithm of standard RRT to meet the need of collision avoidance and achieve continuous sampling effectively. Xue et al (2018) also proposed a strategy of the control of hybrid gliders that merge the coordinate control model based on artificial potential fields with a motion optimization method.…”

Section: Introductionmentioning

confidence: 99%

A Glider Simulation Model Based on Optimized Support Vector Regression for Efficient Coordinated Observation

Zhuang

Yang

et al. 2021

Front. Mar. Sci.

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Multi-gliders have been widely deployed as an array in nowadays ocean observation for fine and long-term ocean research, especially in deep-sea exploration. However, the strong, variable ocean currents and the delayed information feedback of gliders are remaining huge challenges for the deployment of glider arrays which may cause that the observed data cannot meet the study needs and bring a prohibitive cost. In this paper, we develop a Glider Simulation Model (GSM) based on the support vector regression with the particle swarm optimization (PSO)-SVR algorithm to integrate the information feedback from gliders and ocean current data for rapid modeling to effectively predict the gliders’ trajectories. Based on the real-time predictive information of the trajectories, each glider can select future movement strategies. We utilize the in-suit datasets obtained by sea-wing gliders in ocean observation to train and test the simulation model. The results show that GSM has an effective and stable performance. The information obtained from the modeling approaches can be utilized for the optimization of the deployment of the glider arrays.

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Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments

Cited by 24 publications

References 42 publications

Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments

Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments

Adaptive Visual Information Gathering for Autonomous Exploration of Underwater Environments

A Glider Simulation Model Based on Optimized Support Vector Regression for Efficient Coordinated Observation

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