TY - JOUR
T1 - Multi-robot formation control
T2 - a comparison between model-based and learning-based methods
AU - Jiang, Chao
AU - Chen, Zhuo
AU - Guo, Yi
N1 - Publisher Copyright:
© 2019, © 2019 Northeastern University, China.
PY - 2020/1/2
Y1 - 2020/1/2
N2 - Formation control of multi-robot systems has been extensively studied by model-based methods, where analytic control inputs are constructed based on the kinematics and/or dynamics model and the communication graphs of the multi-robot system. Recently, driven by remarkable advances of robotic learning techniques, emerging studies on learning-based methods for formation control have been developed for adaptive and intelligent control of multi-robot systems. This paper aims to provide a brief overview of our recent development of learning-based formation control, and compare it with a model-based method for a case study of three-robot formation control. Fundamental principles, experimental results and technical challenges are presented, comparing the two different methodologies.
AB - Formation control of multi-robot systems has been extensively studied by model-based methods, where analytic control inputs are constructed based on the kinematics and/or dynamics model and the communication graphs of the multi-robot system. Recently, driven by remarkable advances of robotic learning techniques, emerging studies on learning-based methods for formation control have been developed for adaptive and intelligent control of multi-robot systems. This paper aims to provide a brief overview of our recent development of learning-based formation control, and compare it with a model-based method for a case study of three-robot formation control. Fundamental principles, experimental results and technical challenges are presented, comparing the two different methodologies.
KW - Multi-robot systems
KW - formation control
KW - multi-robot learning
UR - http://www.scopus.com/inward/record.url?scp=85076806387&partnerID=8YFLogxK
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U2 - 10.1080/23307706.2019.1697970
DO - 10.1080/23307706.2019.1697970
M3 - Article
AN - SCOPUS:85076806387
SN - 2330-7706
VL - 7
SP - 90
EP - 108
JO - Journal of Control and Decision
JF - Journal of Control and Decision
IS - 1
ER -