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UAV path planning using potential field and modified receding horizon A* 3D algorithm

Khuswendi T.a, Hindersah H.a, Adiprawita W.a

a Department of Electrical Engineering, Insitut Teknologi 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]Path planning is an important step to design autonomous flight system of UAV. This paper proposes method for designing path planning algorithm that can make UAV move autonomously. The algorithm used potential field method and A* Algorithm. A* algorithm will be created in a 3D environment, and developed to become hierarchical A* 3D algorithm, and receding horizon A*3D algorithm. These proposed algorithms will be tested in GUI simulation. The potential field and A* algorithm are combined, which the potential field is used for setting up environment, while A* algorithm is used to search the optimal path. Each proposed algorithm has advantages and weaknesses, in terms of time and distance travelled. In time viewpoint, the most optimal method is hierarchical A*3D algorithm, as it requires a short processing time but requires larger distance than the others, whereas the most optimal distance is the normal method of A* 3D, but it takes processing time longest, so that the most appropriate method to be implemented on the UAV is receding horizon A*3D algorithm, both in terms of processing time and distance, still visible to be implemented. © 2011 IEEE.[/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]3-D environments,A,Autonomous flight,Optimal methods,Optimal paths,Path-planning,Path-planning algorithm,Potential field,Potential field methods,Processing Time,Receding horizon,Short processing time[/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]A,Algorithm,Path planning UAV,Potential Field[/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/ICEEI.2011.6021579[/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]