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Multilayer Control for Coordinating Three – Wheeled Omnidirectional Mobile Robots
Putra C.S.a, Fahleraz F.a, Widyotriatmo A.a, Mutijarsa K.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, 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]© 2019 IEEE.Operating multiple robots in an environment presents a challenge on how to coordinate each robot to be able to work together while still reaching their maximum performance. To do so, the right control system needs to be designed. On this challenge, we take the RoboCup Middle Size League environment as a case study as it requires both individual robot’s performance as well as good coordination to do well in this setting. Our goal is to design a control system that enables the robots to coordinate, avoid obstacles and collisions, as well as performing velocity tracking to control each of the robot’s movement. We developed a communication framework using socket.io to manage high-level coordination. While we used an A∗-based path generating an algorithm to handle each robot’s navigation. To enable velocity tracking, we used a cascade PID algorithm with acceleration feed-forward. The resulting control system can successfully coordinate and navigate each robot safely. The velocity tracking algorithm is also able to successfully achieve a velocity error of about 1.5% and a distance error of about 2%.[/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]Avoid obstacles,Communication framework,Multilayer control,Multiple robot,Omnidirectional mobile robot,Soccer robot,Velocity errors,Velocity tracking[/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,Feed-forward control,Mobile robot,Multilayer control,Obstacle avoidance,Path planning,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/ICA.2019.8916737[/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]