TY - GEN
T1 - A proposed formation control algorithm for robot swarm based on adaptive fuzzy potential field method
AU - Elkilany, Basma Gh
AU - Abouelsoud, A. A.
AU - Fathelbab, Ahmed M.R.
AU - Ishii, Hiroyuki
N1 - Funding Information:
The authors would like to thank the Egypt-Japan University of Science and Technology (E-JUST) for continuous help and support.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/26
Y1 - 2018/12/26
N2 - The main goal of robot swarm is to maintain formation among their members while avoiding obstacles and tracking a target in the surrounding environment. One popular approach for achieving this goal is the Potential Field Method (PFM). Thus, in this paper, we propose a formation control algorithm based on the PFM and Fuzzy Inference System (FIS). The Proposed PFM is intended to maintain formation, avoid obstacles and track a moving target as well. We add an interaction potential force to maintain formation beside the attractive and repulsive potential forces. Also, we use the FIS to adapt the change of the relative distances among robots in the swam and other entities int he environment. To test the scalability and reliability of the proposed formation control algorithm, simulations of robot swarms using MATLAB software with a different number of robots following different target trajectories in different environment setups are recorded. Results confirm the efficiency and the applicability the proposed formation control algorithm in achieving the three tasks of the robot swarm.
AB - The main goal of robot swarm is to maintain formation among their members while avoiding obstacles and tracking a target in the surrounding environment. One popular approach for achieving this goal is the Potential Field Method (PFM). Thus, in this paper, we propose a formation control algorithm based on the PFM and Fuzzy Inference System (FIS). The Proposed PFM is intended to maintain formation, avoid obstacles and track a moving target as well. We add an interaction potential force to maintain formation beside the attractive and repulsive potential forces. Also, we use the FIS to adapt the change of the relative distances among robots in the swam and other entities int he environment. To test the scalability and reliability of the proposed formation control algorithm, simulations of robot swarms using MATLAB software with a different number of robots following different target trajectories in different environment setups are recorded. Results confirm the efficiency and the applicability the proposed formation control algorithm in achieving the three tasks of the robot swarm.
KW - Formation Control
KW - Fuzzy Inference System
KW - Potential Field Method
KW - Robot Swarm
UR - http://www.scopus.com/inward/record.url?scp=85061539837&partnerID=8YFLogxK
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U2 - 10.1109/IECON.2018.8591383
DO - 10.1109/IECON.2018.8591383
M3 - Conference contribution
AN - SCOPUS:85061539837
T3 - Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
SP - 2189
EP - 2194
BT - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018
Y2 - 20 October 2018 through 23 October 2018
ER -