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Design and implementation of behavior-based coordination system on soccer robot
Hartanto M.I.a, Mutijarsa K.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, West Java, 40132, 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]© 2017 IEEE.Autonomous mobile robot soccer is a very popular field of research. The complexity of robot designs that include perception systems, processing systems, drive systems, inter-subsystem coordination systems, and intelligence in game strategies, make the development of soccer robots even more exciting and challenging. This paper discussed the design results of the coordination system of soccer robot. The coordination system is responsible for integrating information from the perception system into game strategies. Then the game strategy processing generates information to the locomotion system, dribbler, and kicker. The coordination system between subsystems is implemented using behavior-based intelligent systems. This selection is based on intelligent behavior-based system design designed by bottom-up, starting from simple behavior. So it allows researchers to develop it in a sustainable manner. The implementation of this system is using Finite State Machine (FSM) with the help of fuzzy logic as the basis of decision making. This soccer robot coordination system has been implemented on a robot platform named Devara. From the test results in the field in the game, the robot can recognize the ball, move towards the ball, dribble, and kick the ball towards the goal. The average time needed by the robot in the game to find the ball placed in several positions, then dribble and score the goal is 13.2 seconds.[/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]Autonomous Mobile Robot,behavior,Coordination systems,Design and implementations,Integrating information,Intelligent behavior,Perception systems,Soccer robot[/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]behavior,FSM,fuzzy logic,intelligent system,soccer robot[/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/ICITSI.2017.8267941[/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]