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[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]© 2019 IEEE.Research about robot vision is growing and more focusing on using image processing algorithm to enhance the sensing ability of a robot mobile to aware about its surroundings. This research using Kinect sensor which has 2 cameras, one RGB visible light and IR camera / depth camera both with 640×480 pixels at 30fps (frame per second). An object following method is applied on a 4WD differential mobile robot chassis equipped with Kinect sensor 1.0 running at 640×480 pixels, 30fps. The method using pixel coordinate averaging which summing all the pixel coordinate of the object of interest. Each calculation (vertical and horizontal) will result in a single integer value of coordinate in i or j showing the center of the object. Two tests object using symmetrical and non-symmetrical shows that the center object only moves slightly off about 1 pixel on j axis. The result of the calculation for symmetrical object on j axis is 9 (rounded down) and for the non-symmetrical object is 8 (rounded up). with the calculation on i axis is similar (on pixe114).[/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]Averaging method,Differential mobile robots,Image processing algorithm,Image-based,Integer values,Kinect sensors,Sensing abilities,Visible light[/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]center object,kinect,mobile robot,object follower[/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]ACKNOWLEDGMENT The authors would like to express our great thanks to Ministry of Research and Higher Education of Indonesia (MRHE) / Direktorat Jenderal Penguatan Riset dan Pengembangan Kementrian Riset dan Pendidikan Tinggi Republik Indonesia (DRPM Kemenristekdikti) for fully funding this research.[/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/IRCE.2019.00010[/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]