Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment

Das, Bikramaditya; Subudhi, Bidyadhar; Pati, Bibhuti Bhusan

doi:10.1080/20464177.2016.1247636

Cited by 34 publications

(16 citation statements)

References 19 publications

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“…The complexity increased as a double acoustic measurement method is used with "proprioceptive" and "exteroceptive" sensors to avoid fault error solution. Sahu et al [170] developed an algorithm for multiple AUVs′ navigation based on flocking control using leader-follower structure. The leader AUV supplied with the prior knowledge of the intended path, but the follower AUVs don′t have this information.…”

Section: Leader-follower Structurementioning

confidence: 99%

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Panda

Subudhi

Pati

2020

Int. J. Autom. Comput.

Self Cite

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: Leader-follower Structurementioning

confidence: 99%

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Panda

Subudhi

Pati

2020

Int. J. Autom. Comput.

Self Cite

148

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“…In addition to reducing the amount of computation during the heading correction, the LOS algorithm can optimize the trajectory of the AUV turning when the waypoint is changed. Das et al [138] employed a clonal selection optimization algorithm to study the path search algorithm of AUV formation and added the APF algorithm for dynamic obstacle avoidance. Some improved algorithms have appeared to address the problem that this algorithm can easily fall into a local minimum.…”

Section: ) Artificial Potential Field (Apf)mentioning

confidence: 99%

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

137

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An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research. INDEX TERMS AUV, path planning, model building, path search.

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“…The works associated with the use of Multi-Agent Systems (MAS) in maritime environmental and archaeological researches are of particular relevance [6,7]. At present, the issues of control automation of a group of agents using complex onboard navigation devices and receivers of satellite navigation systems, radio channels, etc., have been rather fully investigated [8][9][10]. Such systems use multipurpose collaborative search algorithms, adaptive prediction, and wildlife-based algorithms (dolphins, fishes, etc.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Control Automation of Maritime Unmanned Complex With a Group of Autonomous Underwater Vehicles

Blintsov

Aloba

2019

EUREKA: Physics and Engineering

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It is expedient to perform underwater search operations on large water areas using a group of autonomous self-propelled underwater vehicles. However, with a large distance to the search areas, the sea transition (from one point to the other) of the underwater vehicles requires high energy costs. This leads to the necessity to use heavy-duty underwater vehicles, which determines the high cost of the search operation. The transport of underwater vehicles is proposed to be carried out with an unmanned surface vessel, equipped with actuators for the automatic release of a group of vehicles under water and receiving on board after the end of the underwater mission. The maritime unmanned complex consisting of an unmanned surface vessel and a group of autonomous underwater vehicles on its board forms a new type of marine robotics, the complete automation of which is an actual scientific and technical task. For its implementation, the underlying (basic) automation technology of the marine search underwater mission has been developed as the theoretical basis for the development of the generalized structure of the complex automatic control system. Ten implementation stages of the underlying technology are formulated and the analysis of their automation features with the use of modern methods in the field of marine robotics is performed. Automation of the underlying technology stages involves the transfer of the vessel to a given water area, the automatic release (launch) of the group of underwater vehicles and their coordinated motion to the search area, the search operations and the return to the unmanned surface vessel, as well as the recovery of the vessel to the base. The generalized requirements for automatic control systems constituting the maritime unmanned complex at each stage of its functioning are provided. The spiral trajectory of waiting for the motion of the underwater vehicles at the group formation stages, for the search operation execution and after its completion, is proposed. For the spatial motion of the autonomous underwater vehicle as an agent of the group, the automatic control system was improved by introducing the blocks of the "Navigation Situation Model" and the "Navigation Threat Identifier, which make it impossible for emergency collision with the neighboring underwater vehicles of the group and disintegrate the group due to the data communication loss between them.

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Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment

Cited by 34 publications

References 19 publications

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Control Automation of Maritime Unmanned Complex With a Group of Autonomous Underwater Vehicles

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