A dynamic path planning method for terrain-aided navigation of autonomous underwater vehicles

Ma, Teng; Li, Ye; Jiang, Yanqing; Wang, Rupeng; Zheng, Cong; Yan, Gao

doi:10.1088/1361-6501/aad466

Cited by 12 publications

(7 citation statements)

References 24 publications

(26 reference statements)

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“…Besides, the distance between nodes should be decided by the confidence of the current matching result instead of setting them as a fixed value. Ma Teng 10 made improvements on Steer function and choose parent function in RRT* algorithm. Area of interest (AOI) is proposed for the process searching for the new node.…”

Section: Methodsmentioning

confidence: 99%

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An evaluation of path-planning methods for autonomous underwater vehicle based on terrain-aided navigation

Zheng

Jiang

et al. 2019

International Journal of Advanced Robotic Systems

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This article presents a comparison of different path-planning algorithms for autonomous underwater vehicles using terrain-aided navigation. Four different path-planning methods are discussed: the genetic algorithm, the A* algorithm, the rapidly exploring random tree* algorithm, and the ant colony algorithm. The goal of this article is to compare the four methods to determine how to obtain better positioning accuracy when using terrain-aided navigation as a means of navigation. Each algorithm combines terrain complexity to comprehensively consider the motion characteristics of the autonomous underwater vehicles, giving reachable path between the start and end points. Terrain-aided navigation overcomes the challenges of underwater domain, such as visual distortion and radio frequency signal attenuation, which make landmark-based localization infeasible. The path-planning algorithms improve the terrain-aided navigation positioning accuracy by considering terrain complexity. To evaluate the four algorithms, we designed simulation experiments that use real-word seabed bathymetry data. The results of autonomous underwater vehicle navigation by terrain-aided navigation in these four cases are obtained and analyzed.

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Section: Methodsmentioning

confidence: 99%

“…Whenever a new match result is obtained, we use a particle filter (PF) to fuse the navigation information to get the real-time position of the AUV. The PF design and parameter selection are referred from Teng et al 10…”

Section: Tan Problemmentioning

confidence: 99%

An evaluation of path-planning methods for autonomous underwater vehicle based on terrain-aided navigation

Zheng

Jiang

et al. 2019

International Journal of Advanced Robotic Systems

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“…Main () Initialize RRT (tree); Grow RRT (tree); Hernandez et al [108] proposed a method using workdomain information to identify "homotopy classes". These classes graphically describe the paths going through the obstacles in the 2D work-domain.…”

Section: Rrtmentioning

confidence: 99%

“…•Time optimized trajectory with fast convergence speed and less computational cost •The search space is restricted Unpredictable RRT [105][106][107][108][109] Time optimal Achieved Low…”

Section: Achieved Lowmentioning

confidence: 99%

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Panda

Subudhi

Pati

2020

Int. J. Autom. Comput.

141

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The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments. The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles (AUVs) because of its hostile and dynamic nature. The major constraints for path planning are limited data transmission capability, power and sensing technology available for underwater operations. The sea environment is subjected to a large set of challenging factors classified as atmospheric, coastal and gravitational. Based on whether the impact of these factors can be approximated or not, the underwater environment can be characterized as predictable and unpredictable respectively. The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner. But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted. Path planning is necessary for many applications involving AUVs. These are based upon planning safety routes with minimum energy cost and computation overheads. This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable. The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.

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“…Moreover, problems with aero-optical effects and stray light induced by complicated flow fields near the vehicle are yet to be resolved (Lu and Yang, 2018;Zhang et al, 2018). Inertial/terrain integrated navigation (SINS/TAN) is not applicable in areas with minor topographical fluctuations (Ma et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Comparison of geomagnetic aided navigation algorithms for hypersonic vehicles

Chen

Liang

Liu

et al. 2020

J. Zhejiang Univ. Sci. A

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In this paper, we simulate, verify, and compare the performance of three classical geomagnetic matching aided navigation algorithms to assess their applicability to hypersonic vehicle navigation. Firstly, we introduce the various sources of the geomagnetic field. Secondly, we describe the principles and processes of the geomagnetic contour matching (MAGCOM) algorithm, iterative closest contour point (ICCP) algorithm, and Sandia inertial magnetic aided navigation (SIMAN) algorithm. Thirdly, we discuss the principles of inertial/geomagnetic integrated navigation, and propose the state and observation equations of integrated navigation. Finally, we perform a simulation of inertial/geomagnetic integrated navigation on the hypersonic boost-glide vehicle trajectory. The simulation results indicate that the real-time performance of the SIMAN algorithm can be optimized such that the matching accuracy is higher than that of the other two algorithms. At the same time, the SIMAN algorithm can achieve better stability, and though the amount of measurement noise can be larger, it can still achieve good positioning accuracy.

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A dynamic path planning method for terrain-aided navigation of autonomous underwater vehicles

Cited by 12 publications

References 24 publications

An evaluation of path-planning methods for autonomous underwater vehicle based on terrain-aided navigation

An evaluation of path-planning methods for autonomous underwater vehicle based on terrain-aided navigation

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Comparison of geomagnetic aided navigation algorithms for hypersonic vehicles

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