Efficient reacquisition path planning for multiple autonomous underwater vehicles

Stack, J.R.; Smith, Clifford E.; Hyland, John C.

doi:10.1109/oceans.2004.1406355

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…The receiver AUV uses this information along with its own DR data to predict its location. It has been verified experimentally that a system combining LBL acoustic navigation data with Doppler navigation data [147] provides superior navigation precision and update rates in comparison to individual LBL or Doppler navigation alone (Fig. 24).…”

Section: Dead Reckoning (Dr) and Long-baseline (Lbl)mentioning

confidence: 89%

“…Constrained system AUV Position constraints Fig. 23 Path planning controller virtually constrained system [145] 12 kHz LBL [147] ( Fig. 25).…”

Section: Constrained Stabilizationmentioning

confidence: 99%

“…•The required communication variables are minimized and the knowledge of network layout is not necessary to be known by everyone •Computational complexity is high Predictable Lagrangian multipliers [144] Time optimal Poor Low [145][146][147][148][149] Time optimal Limited High…”

Section: Achieved Lowmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Panda

Subudhi

Pati

2020

Int. J. Autom. Comput.

148

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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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Section: Dead Reckoning (Dr) and Long-baseline (Lbl)mentioning

confidence: 89%

“…Constrained system AUV Position constraints Fig. 23 Path planning controller virtually constrained system [145] 12 kHz LBL [147] ( Fig. 25).…”

Section: Constrained Stabilizationmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Panda

Subudhi

Pati

2020

Int. J. Autom. Comput.

148

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“…The waypoint reacquisition algorithm provides a computationally efficient task assignment method [1]. This approach is accomplished by employing a cluster-first, route-second heuristic technique with no feedback or iterations between the clustering and route building steps.…”

Section: Introductionmentioning

confidence: 99%

“…This paper extends the Ant Colony System (ACS) into Multiple Ant Colonies System (MACS). The purpose is to construct Zhenzhen Xu 1,2 , Yiping Li 1,2 , Xisheng Feng 1 multiple ant colonies to optimize multiple objectives respectively, and to find the solution closest to the ideal solution from the set of Pareto optimal solutions through the interaction among different ant colonies. The reminder of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Constrained Multi-objective Task Assignment for UUVs Using Multiple Ant Colonies System

Feng

2008

2008 ISECS International Colloquium on Computing, Communication, Control, and Management

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Cooperation Between Underwater Vehicles

Redfield

2012

Marine Robot Autonomy

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Efficient reacquisition path planning for multiple autonomous underwater vehicles

Cited by 9 publications

References 8 publications

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Constrained Multi-objective Task Assignment for UUVs Using Multiple Ant Colonies System

Cooperation Between Underwater Vehicles

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