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Flocking for nonholonomic robots with obstacle avoidance
Burohman A.M.a, Widyotriatmo A.a, Joelianto E.a
a Instrumentation and Control Master Program, Bandung Institute of Technology (ITB), 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]© 2016 IEEE.This paper proposes the algorithm for flocking of group of nonholonomic mobile robots. Three protocols of smooth potential function are implemented, i.e. flocking, obstacle avoidance, and attracting to goal, as variables of velocity. The main scheme of control of the mobile robots is the use of trajectory tracking which is generated in order to obtain flocking behavior, collision and obstacle avoidance, and reaching the goal in cohesion by flocking. The convergence for flocking algorithm for single integrator dynamic systems is shown by using Lyapunov stability theorem to converge to the local minimum of flock and to the goal point. Simulation results perform that mobile robots can split, rejoin and then reach the goal. Moreover, the group of mobile robots can accomplish flocking through narrow space without collision with obstacles and among mobile robots.[/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]Collision with obstacle,flocking,Flocking algorithms,Lyapunov stability theorem,Non-holonomic mobile robots,Potential function,Trajectory generation,Trajectory 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]collision avoidance,flocking,flocking algorithm stability,Smooth potential function,trajectory generation[/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 thank for the support from The Ministry of Research, Technology and Higher Education of the Republic of Indonesia under the Decentralized Research Program on Excellent Research University, Bandung Institute of Technology, Bandung, Indonesia 2016.[/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/ELECSYM.2016.7861029[/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]