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Route optimization movement of tugboat with A∗ tactical pathfinding in SPIN 3D simulation
Anisyah A.S.a, Rusmin P.H.a, Hindersah H.a
a School of Electrical and Informatics Engineering, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 IEEE.Ships as one of the favorite transport is now a tool of choice, especially for the transport of goods in container. A relatively low cost with a large capacity are the main reasons why the vessel is the best choice. It is of course also affect the port density of the traffic managers of trade. The concerned problem in this research focus on optimization route for tugboat. The goal is to calculate the total cost for fuel and total distance that tugboat explored. Using A∗ algorithm tactical pathfinding with navigation mesh will draw an optimization route for tugboat to berthing or leave from port. Two activity that mention before are the main responsibility for the tugboat which used as parameter to calculate the total distance that tugboat explored. In the end of this research will find out how the route affect the total of distance without reduce the service for the vessel berth in port. Dataset which used in this research are port of Tanjung Priok with some parameters for unwalkable area for A∗ agent. The obstacle in this simulation are the position of other ships in the queue, the position of other ships in the docks and obstacles in the port area such as coral and anything that can be dangerous for the ship docked. From this research, optimize path success to reduce the distance compare to simulation without optimize route.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]3D simulations,Best choice,Pathfinding,Port areas,Research focus,Route optimization,Total distances,Traffic managers[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Navigation Mesh,pathfinding,Port simulator,Tactical Pathfinding[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/IDM.2015.7516319[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]