Area Decomposition Algorithm for Large Region Maritime Search

Xing, Shengwei; Wang, Renda; Huang, Gang

doi:10.1109/access.2020.3037679

Cited by 7 publications

(5 citation statements)

References 42 publications

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“…However, choosing the best search facilities is only one step in the maritime search operation. The next step is to determine specific search subareas for these search facilities [41]. The region of maritime search operations is usually represented by polygons.…”

Section: Discussionmentioning

confidence: 99%

Optimal Search Facilities Selection Model for Joint Aeronautical and Maritime Search

Xing

2021

IEEE Access

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Joint aeronautical and maritime search and rescue is the most effective way of performing rescues at sea. The value and effectiveness of a search and rescue (SAR) are far greater when using a coordinated air-maritime search than when using only vessels or aircraft. However, the harmonization of aeronautical and maritime SAR is complex and potentially life-threatening. When the location of the target in distress is unknown, the search process must be carried out. As the sole way to locate and rescue survivors, the search process is the most costly, hazardous, and complicated part of the whole SAR operation. This paper focuses on the key problem of the optimal selection of search facilities, that is often encountered in largearea maritime search practice and urgently needs to be solved in joint aeronautical and maritime search operations. The problem may be abstracted into an optimization model with vessel and aircraft quantitative constraints that fully considers the area of the sea region to be searched, maximum speeds, search capabilities, initial distances of vessels and aircraft from the search area, and maximum endurance of aircraft. By introducing 0-1 decision variables, the search facility selection can be judged and optimized directly and effectively. By analyzing the results with different vessel and aircraft quantities, and taking the relationship between search coverage time and the number of search facilities (cost) into account, the optimal (most economic and feasible) search facility selection scheme can be produced. INDEX TERMS Joint aeronautical and maritime search, marine safety, search facility, optimal model.

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Section: Discussionmentioning

confidence: 99%

Optimal Search Facilities Selection Model for Joint Aeronautical and Maritime Search

Xing

2021

IEEE Access

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“…In addition to large projects, sonar, photoelectric, and other sensors are used in many places to search for the extent of designated water bodies to enable identification, localization, and demining. It is possible to search for coverage using a circuit algorithm [ 140 , 141 ] for target waters with regular shapes and known environments, and frontier-based methods [ 142 ] and improved algorithms are mainly used [ 143 , 144 , 145 , 146 ] for complex unknown environments with dynamic obstacles. Methods for measuring the depth of the water against the natural sea using autonomous/unmanned irradiation lines operating on global navigation satellite system (GNSS) measurements or satellite images have also been developed [ 147 ].…”

Section: Intelligencementioning

confidence: 99%

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Bae

Hong

2023

Sensors

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With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development.

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“…Ardupilot SITL instances validated the results in the ROS framework. In recent proposals, area partitioning has token relevance for maritime applications [17]. The work uses a polygon decomposition algorithm to carry out complete search coverage.…”

Section: Polygon-based Coverage Plansmentioning

confidence: 99%

MCO Plan: Efficient Coverage Mission for Multiple Micro Aerial Vehicles Modeled as Agents

Campo

Ledezma

Corrales

2022

Drones

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Micro aerial vehicle (MAV) fleets have gained essential recognition in the decision schemes for precision agriculture, disaster management, and other coverage missions. However, they have some challenges in becoming massively deployed. One of them is resource management in restricted workspaces. This paper proposes a plan to balance resources when considering the practical use of MAVs and workspace in daily chores. The coverage mission plan is based on five stages: world abstraction, area partitioning, role allocation, task generation, and task allocation. The tasks are allocated according to agent roles, Master, Coordinator, or Operator (MCO), which describe their flight autonomy, connectivity, and decision skill. These roles are engaged with the partitioning based on the Voronoi-tessellation but extended to heterogeneous polygons. The advantages of the MCO Plan were evident compared with conventional Boustrophedon decomposition and clustering by K-means. The MCO plan achieved a balanced magnitude and trend of heterogeneity between both methods, involving MAVs with few or intermediate resources. The resulting efficiency was tested in the GAMA platform, with gained energy between 2% and 10% in the mission end. In addition, the MCO plan improved mission times while the connectivity was effectively held, even more, if the Firefly algorithm generated coverage paths.

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Area Decomposition Algorithm for Large Region Maritime Search

Cited by 7 publications

References 42 publications

Optimal Search Facilities Selection Model for Joint Aeronautical and Maritime Search

Optimal Search Facilities Selection Model for Joint Aeronautical and Maritime Search

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

MCO Plan: Efficient Coverage Mission for Multiple Micro Aerial Vehicles Modeled as Agents

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