Circular Formation Algorithms for Multiple Nonholonomic Mobile Robots: An Optimization-Based Approach

Qin, Jiahu; Wang, Shuai; Kang, Yu; Liu, Qingchen

doi:10.1109/tie.2018.2864721

Cited by 38 publications

(39 citation statements)

References 24 publications

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“…This raises an interesting control problem that a positive forward motion must be maintained all the time. To solve this problem, some papers, e.g., [5], [6], [7] assume that the linear speed is a positive constant and mainly focus on the design of angular speed input. However, there are limited results that take both linear speed and angular speed constraints into the design consideration.…”

Section: Introductionmentioning

confidence: 99%

Mobile Formation Coordination and Tracking Control for Multiple Nonholonomic Vehicles

Peng

Sun

Guo

et al. 2020

IEEE/ASME Trans. Mechatron.

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This paper addresses forward motion control for trajectory tracking and mobile formation coordination for a group of non-holonomic vehicles on SE(2). Firstly, by constructing an intermediate attitude variable which involves vehicles' position information and desired attitude, the translational and rotational control inputs are designed in two stages to solve the trajectory tracking problem. Secondly, the coordination relationships of relative positions and headings are explored thoroughly for a group of non-holonomic vehicles to maintain a mobile formation with rigid body motion constraints. We prove that, except for the cases of parallel formation and translational straight line formation, a mobile formation with strict rigid-body motion can be achieved if and only if the ratios of linear speed to angular speed for each individual vehicle are constants. Motion properties for mobile formation with weak rigid-body motion are also demonstrated. Thereafter, based on the proposed trajectory tracking approach, a distributed mobile formation control law is designed under a directed tree graph. The performance of the proposed controllers is validated by both numerical simulations and experiments.

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Section: Introductionmentioning

confidence: 99%

Mobile Formation Coordination and Tracking Control for Multiple Nonholonomic Vehicles

Peng

Sun

Guo

et al. 2020

IEEE/ASME Trans. Mechatron.

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“…Recently, with the rapid developments of digital systems and information, communication and sensing technologies, distributed optimization problems of complex cyber‐physical networks have received considerable attention due to their wide applications in machine learning of sensor networks, energy management of smart grids, formation control of robotic systems, and so on 1‐7 . A fundamental topic in the distributed optimization problems is to design a distributed algorithm for a group of nodes to cooperatively seek a minimizer of a global function, which is a sum of local functions possessed by each node, through merely local communication.…”

Section: Introductionmentioning

confidence: 99%

Resilient consensus‐based distributed optimization under deception attacks

Qin

et al. 2020

Intl J Robust & Nonlinear

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Summary This article considers distributed optimization problems of complex cyber‐physical networks, whose goal is to minimize a global function consisting of a sum of local functions possessed by each node, when the communication network is suffering L‐local deception attacks. After showing that merely 1‐local deception attacks can arbitrarily affect the outcome of any distributed optimization algorithms without being detected, we propose a resilient consensus‐based distributed optimization algorithm, where the estimation for the optimizer of each node is updated according to its subgradient and its partial neighbors' estimation. Then, we provide the conditions for the proposed algorithm to ensure that all the nodes can make an agreement and converge to the convex hull of the local optimizer of their functions in the presence of L‐local deception attacks. Finally, some simulation examples are presented to demonstrate the effectiveness of the proposed algorithm.

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“…This topic has many wide potential applications such as distributed consensus, formation control, sensor networks, swarming and flocking control, and air traffic control. [1][2][3] In this literature, two approaches are commonly used to control MAS, that is, a centralized control and a distributed control. The centralized control is based on the assumption that a global central controller is available to control MAS.…”

Section: Introductionmentioning

confidence: 99%

Optimal topology design and distributed formation control of multiagent systems with complex‐weights directed networks

Ghasemi

Menhaj

Askari

et al. 2020

Optim Control Appl Methods

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Summary In this article, we consider the optimal topology design and distributed formation control problem of multiagent systems (MAS) with complex‐weighted directed topology. First, a framework is proposed to associate optimal topology of MAS to a constrained optimization problem with a complex Laplacian matrix, which is independent of the agent dynamics. The main contribution of the proposed approach compared with existing results is that the proposed approach does not require the calculation of the stabilizing matrix such that the closed‐loop system is asymptotically stable, and a unique set of complex weights that satisfy associated cooperative conditions can be chosen. Then, a distributed formation control protocol is proposed to enable all agents to achieve the control goal. Finally, some numerical example results are provided to demonstrate the effectiveness of the proposed scheme.

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Circular Formation Algorithms for Multiple Nonholonomic Mobile Robots: An Optimization-Based Approach

Cited by 38 publications

References 24 publications

Mobile Formation Coordination and Tracking Control for Multiple Nonholonomic Vehicles

Mobile Formation Coordination and Tracking Control for Multiple Nonholonomic Vehicles

Resilient consensus‐based distributed optimization under deception attacks

Optimal topology design and distributed formation control of multiagent systems with complex‐weights directed networks

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