A survey of multi-agent formation control

Oh, Kwang‐Kyo; Park, Myoung-Chul; Ahn, Hyo‐Sung

doi:10.1016/j.automatica.2014.10.022

Cited by 2,008 publications

(1,052 citation statements)

References 112 publications

(140 reference statements)

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“…Distributed formation control solutions are also abundant in the literature [6]. The restriction of having a central planner can be easily lifted using nearest neighbors information [7].…”

Section: A Related Workmentioning

confidence: 99%

Distributed multi-robot formation control among obstacles: A geometric and optimization approach with consensus

Alonso–Mora¹,

Montijano

Schwager

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-This paper presents a distributed method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The robots are assumed to have a reduced communication and visibility radius and share information with their neighbors. Via distributed consensus the robots compute (a) the convex hull of the robot positions and (b) the largest convex region within free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation within this convex neighborhood of the robots. Reconfiguration is allowed, when required, by considering a set of target formations. The robots navigate towards the target collision-free formation with individual local planners that account for their dynamics. The approach is efficient and scalable with the number of robots and performs well in simulations with up to sixteen quadrotors. I. INTRODUCTIONMuti-robot teams can be employed for various tasks, such as surveillance, inspection, or automated factories. In these scenarios, robots may be required to navigate in formation, for example for maintaining a communication network, for collaboratively handling an object, for surveilling an area or to improve navigation.Multi-robot navigation in formation has received extensive attention in the past, with many works considering obstaclefree scenarios. In [1] we leveraged efficient optimization techniques, namely quadratic programming, semi-definite programming and (non-linear) sequential quadratic programming to devise a centralized method for local navigation in formation. These techniques provided good computational efficiency, local guarantees and generality, and were employed at different stages of the method for local motion planning in formation among static and dynamic obstacles, albeit centralized.In this work we combine the optimization concepts presented in [1] with distributed consensus and geometric reasoning to achieve similar results in a distributed schema, where robots are no more centrally controlled, but instead have a limited field of view, and communicate with their immediate neighbors.

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“…Distributed formation control solutions are also abundant in the literature [6]. The restriction of having a central planner can be easily lifted using nearest neighbors information [7].…”

Section: A Related Workmentioning

confidence: 99%

Distributed multi-robot formation control among obstacles: A geometric and optimization approach with consensus

Alonso–Mora¹,

Montijano

Schwager

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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“…In the area of multi-agent systems, formation control, which consists in controlling multiple agents to achieve their goal and meet their constraints, is one of the most actively studied topics [14]. Various strategies and approaches were studied for solving the motion coordination and formation control problems, which can be classified in three categories as it follows: 1) leaderfollower approach [15], [16], [17], [18], [19], 2) virtual structure approach [20], [21], 3) behavior based approach [22], [23], and [24].…”

Section: A Related Workmentioning

confidence: 99%

Distributed model predictive control for unmanned aerial vehicles

Mansouri

Nikolakopoulos

Gustafsson

2015

2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

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Abstract-In this article a distributed model predictive control scheme, for the cooperative motion control of Unmanned Aerial Vehicles (UAVs) is being presented. The UAVs are modeled by a 6-DOF nonlinear kinematic model. Two di↵erent control architectures: a centralized and a distributed MPC, are studied and evaluated in simulation experiments. In the centralized approach, one central MPC controller is responsible for the movement coordination of all the UAVs, while in the distributed approach each aerial vehicle plans only for its own actions, while the objective function is coupled with the behavior of the rest of the team members and the constraints are decoupled. In this approach, each agent only shares the future position of itself with the other agents to avoid collisions. For reducing the computation time and complexity, only one step ahead prediction in the corresponding MPC schemes have been considered without a loss of generality. Finally, the e ciency of the overall suggested decentralized MPC scheme, as well as it comparison with the centralized approach, is being evaluated through the utilization of multiple simulation scenarios.

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“…Different control strategies have been used for formation control, such as actual leader [15], virtual leader [16], behavioural [17], etc. A survey on various classifications of formation control is given in [18]. Consensus based formation control strategies have also been investigated [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Formation control with disturbance rejection for a class of Lipschitz nonlinear systems

Wang

Gong

Ding

2017

Sci. China Inf. Sci.

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In this paper, we consider the leader-follower formation control problem for general multi-agent systems with Lipschitz nonlinearity and unknown disturbances. To deal with the disturbances, a disturbance observer-based control strategy is developed for each follower. Then, a time-varying formation protocol is proposed based on the relative state of the neighbouring agents and sufficient conditions for global stability of the formation control are identified using Lyapunov method in the time domain. The proposed strategy and analysis guarantee that all signals in the closed-loop dynamics are uniformly ultimately bounded and the formation tracking error converges to an arbitrarily small residual set. Finally, the validity of the proposed controller is demonstrated through a numerical example.

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A survey of multi-agent formation control

Cited by 2,008 publications

References 112 publications

Distributed multi-robot formation control among obstacles: A geometric and optimization approach with consensus

Distributed multi-robot formation control among obstacles: A geometric and optimization approach with consensus

Distributed model predictive control for unmanned aerial vehicles

Formation control with disturbance rejection for a class of Lipschitz nonlinear systems

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