Time-varying formation control for unmanned aerial vehicles with switching interaction topologies

Dong, Xiwang; Zhou, Yan; Zhang, Ren; Zhong, Yisheng

doi:10.1016/j.conengprac.2015.10.001

Cited by 310 publications

(137 citation statements)

References 40 publications

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“…Decentralized communication topologies are still mostly used in the realm of theoretical research [57], [59]. Very limited experimental results are presented in the literature [228], [229].…”

Section: Communication Infrastructurementioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: Communication Infrastructurementioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…In the application domain of military and civilian, UAV is regarded as an effective mode to solve many complex problems [5]. Particularly, in recent years, UAV has been successfully used for military purposes, with the outstanding performance in the Gulf War and the war in Afghanistan.…”

Section: Introductionmentioning

confidence: 99%

A novel fast target tracking method for UAV aerial image

2017

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Unmanned aerial vehicles (UAV) are able to achieve autonomous flight without drivers, and UAV has been a key tool to extract space data. Therefore, how to detect the trajectories of targets from UAV aerial image sequences is of great importance. Because local features are suitable to detect target tracking, we exploit scaleinvariant feature transform (SIFT) features to describe the interesting keypoints of targets. The main innovation of this paper is to utilize Multiple hypothesis tracking (MHT) algorithm to track an object (target) in a series of image sequences. Particularly, we develop a MHT framework based on a multidimensional assignment formulation and a sliding time window policy. To obtain target tracking from UAV aerial image sequences, three steps should be done, that is, 1) Breaking each track set into tracklet at a specific time, 2) Estimating the association cost of each track set, 3) Merging trajectory fragments to a longer one iteratively. Finally, we collect several UAV aerial image sequences with different target density to construct a dataset, and experimental results demonstrate the effectiveness of the proposed algorithm.

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“…Consensus and stability are widely accepted to investigate the agents' dynamical behaviors, and meanwhile which are also premise and fundamental problem of the cooperative control for complex systems [9,10,12]. Consensus means that all the agents' states converge to the same value, based on their interaction or feedback protocol.…”

Section: Introductionmentioning

confidence: 99%

H_? Consensus Control of Multi-Agent Systems: a Static Output Feedback Controller with Time-Delay

Dun¹,

Yang²

2018

Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)

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Abstract-This paper addresses the consensus problem for networks of multi-agent systems subject to external disturbances. In particular, a distributed static output feedback controller with time-varying delays is provided, which based on the relative out-puts of neighboring agents. By using Schur complement lemma, Newton Leibniz formula and Jensen's inequality, sufficient conditions are derived to guarantee that consensus is achieved under an undirected communication topology, which are in terms of linear matrix inequalities (LMIs). And meanwhile the parameter of controller is also determined. Finally, a numerical example is presented to illustrate the effectiveness of the proposed method.

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Time-varying formation control for unmanned aerial vehicles with switching interaction topologies

Cited by 310 publications

References 40 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

A novel fast target tracking method for UAV aerial image

H_? Consensus Control of Multi-Agent Systems: a Static Output Feedback Controller with Time-Delay

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