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An overview of recent progress in the study of
distributed multi-agent coordination
Cao, Yongcan; Yu, Wenwu; Ren, Wei; Chen, Guanrong
https://researchrepository.rmit.edu.au/discovery/delivery/61RMIT_INST:ResearchRepository/12246869400001341?l#13248385820001341
Cao, Y., Yu, W., Ren, W., & Chen, G. (2013). An overview of recent progress in the study of distributed
multi-agent coordination. IEEE Transactions on Industrial Informatics, 9(1), 427–438.
https://doi.org/10.1109/TII.2012.2219061
Published Version: https://doi.org/10.1109/TII.2012.2219061
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© 2005-2012 IEEE.
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PLEASE DO NOT REMOVE THIS PAGE
Cao, Y, Yu, W, Ren, W and Chen, G 2013, 'An overview of recent progress in the study of
distributed multi-agent coordination', IEEE Transactions on Industrial Informatics, vol. 9, no.
1, pp. 427-438.
http://researchbank.rmit.edu.au/view/rmit:20438
A
ccepted Manuscript
2005-2012 IEEE.
http://researchbank.rmit.edu.au/view/rmit:20438
1
An Overview of Recent Progress in the Study
of Distributed Multi-agent Coordination
Yongcan Cao, Member, IEEE, Wenwu Yu, Member, IEEE,
Wei Ren, Member, IEEE, and Guanrong Chen Fellow, IEEE
Abstract
This article reviews some main results and progress in distributed multi-agent coordination, with
the focus on papers published in major control systems and robotics journals since 2006. Distributed
coordination of multiple vehicles, including unmanned aerial vehicles (UAVs), unmanned ground vehicles
(UGVs) and unmanned underwater vehicles (UUVs), has been a very active research subject studied
extensively by the systems and control community. The recent results in this area are categorized into
several directions, such as consensus, formation control, optimization, distributed task assignment, and
estimation. After the review, a short discussion section is included to summarize the existing research
and to propose several promising research directions along with some open problems that are deemed
important therefore deserving further investigations.
Index Terms
Distributed coordination, formation control, sensor network, multi-agent system
I. INTRODUCTION
Control theory and practice may date back to the beginning of the last century when Wright Brothers
attempted their first test flight in 1903. Since then, control theory has gradually gained popularity, receiving
more and wider attention especially during the World War II when it was developed and applied to fire-
control systems, missile navigation and guidance, as well as various electronic automation devices. In
This work was supported by ......, and the Hong Kong RGC under GRF Grant CityU1114/11E.
Y. Cao and W. Ren are with the Department of Electrical and Computer Engineering, Utah State University, Logan, Utah
84322, USA. W. Yu is with the Department of Mathematics, Southeast University, Nanjing 210096, China. G. Chen is with the
Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR, China.
Manuscript submitted to IEEE Transactions on Industrial Informatics on 31 July 2011.
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the past several decades, modern control theory was further advanced due to the booming of aerospace
technology based on large-scale engineering systems.
During the rapid and sustained development of the modern control theory, technology for controlling
a single vehicle, albeit higher-dimensional and complex, has become relatively mature and has produced
many effective control tools such as PID control, adaptive control, nonlinear control, intelligent control,
and robust control methodologies. In the past two decades in particular, control of multiple vehicles
has received increasing demands spurred by the fact that many benefits can be obtained when a single
complicated vehicle is equivalently replaced by multiple yet simpler vehicles. In this endeavor, two ap-
proaches are commonly adopted for controlling multiple vehicles: a centralized approach and a distributed
approach. The centralized approach is based on a basic assumption that a central station is available and
powerful enough to control a whole group of vehicles. Essentially, the centralized approach is a direct
extension of the traditional single-vehicle-based control philosophy and strategy. On the contrary, the
distributed approach does not require a central station for control, at the cost of becoming far more
complex than the centralized one in structure and organization. Although both approaches are considered
practical depending on the situations and conditions of the real applications, the distributed approach
is believed more promising due to many inevitable physical constraints such as limited resources and
energy, short wireless communication ranges, narrow bandwidths, and large sizes of vehicles to manage
and control. Therefore, the focus of this overview is placed on the distributed approach.
In distributed control of a group of autonomous vehicles such as UAVs, UGVs and UUVs, the main
objective typically is to have the whole group of vehicles working in a cooperative fashion throughout
a distributed protocol. Here, cooperative refers to a close relationship among all vehicles in the group
where information sharing plays a central role. The distributed approach has many advantages in achiev-
ing cooperative group performances, especially with low operational costs, less system requirements,
high robustness, strong adaptivity, and flexible scalability, therefore has been widely recognized and
appreciated.
The study of distributed control of multiple vehicles was perhaps first motivated by the work in
distributed computing [1], management science [2], [3], and statistical physics [4]. In the control systems
society, some pioneering works are generally referred to [5], [6], where an asynchronous agreement
problem was studied for distributed decision-making problems. Thereafter, some consensus algorithms
were studied under various information-flow constraints [7]–[11]. There are several journal special issues
on the related topics published after 2006, including the IEEE Transactions on Control Systems Technol-
ogy (vol. 15, no. 4, 2007), Proceedings of the IEEE (vol. 94, no. 4, 2007), ASME Journal of Dynamic
July 31, 2011 DRAFT
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Systems, Measurement, and Control (vol. 129, no. 5, 2007), SIAM Journal of Control and Optimization
(vol. 48, no.1, 2009), and International Journal of Robust and Nonlinear Control (Vol. 21, no. 12, 2011).
In addition, there are some more recent reviews and progress reports given in the surveys [12]–[15] and
the books [16]–[21].
This article reviews some main results and recent progress in distributed multi-agent coordination,
published in major control systems and robotics journals since 2006. For results before 2006, the readers
are referred to [12]–[15].
Specifically, this article reviews the recent research results in the following directions, which are not
independent but actually may have overlapping to some extent:
1. Consensus and the like (synchronization, rendezvous). Consensus refers to the group behavior that
all the agents asymptotically reach a certain common agreement through a local distributed protocol,
with or without predefined common speed and orientation.
2. Distributed formation and the like (flocking). Distributed formation refers to the group behavior
that all the agents form a pre-designed geometrical configuration through local interactions with or
without a common reference.
3. Distributed optimization. This refers to algorithmic developments for the analysis and optimization
of large-scale distributed systems.
4. Distributed task assignment. This refers to the implementation of a task-assignment algorithm in a
distributed fashion based on local information.
5. Distributed estimation and control. This refers to distributed control design based on local estimation
about the needed global information.
The rest of this article is organized as follows. In Section II, basic notations of graph theory and
stochastic matrices are introduced. Sections III, IV, V, VI, and VII describe the recent research results
and progress in consensus, formation control, optimization, task assignment, and estimation, respectively.
Finally, the article is concluded by a short section of discussions with future perspectives.
II. PRELIMINARIES
This section introduces basic concepts and notations of graph theory and stochastic matrices.
A. Graph Theory
For a system of n connected agents, its network topology may be modeled as a directed graph denoted
G = (V, W), where V = {v
1
, v
2
, ··· , v
n
} and W ⊆ V ×V are, respectively, the set of agents and the set
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