Multi-Agent Formation Tracking for Autonomous Surface Vehicles

Ringbäck, Rasmus; Wei, Jieqiang; Erstorp, Elias Strandell; Kuttenkeuler, Jakob; Johansen, Tor Arne; Johansson, Karl Henrik

doi:10.1109/tcst.2020.3035476

Cited by 44 publications

(15 citation statements)

References 27 publications

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“…We show notably that in the sensorcentric point of view, the dynamics of the robot is not important for our concept of observability and only its state and input trajectories matter. Besides, the notion of circumnavigation appears to be useful in SLAM and related problems of localisation of autonomous systems, see [11,17,16,38,40,41]. Inspired by this, we finally show that 2D SLAM systems with different types of sensors can be made simultaneously weakly-regularly observable if the robot tracks any circular trajectory in the position/velocity space.…”

Section: Introductionmentioning

confidence: 61%

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Non-uniform Observability for Moving Horizon Estimation and stability with respect to additive perturbation

Flayac¹,

Shames²

2021

Preprint

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This paper first formalises a new observability concept, called weak regular observability, that is adapted to Fast Moving Horizon Estimation where one aims to estimate the state of a nonlinear system efficiently on rolling time windows in the case of small initial error. Additionally, sufficient conditions of weak regular observability are provided in a problem of Simultaneous Localisation and Mapping (SLAM) for different measurement models. In particular it is shown that following circular trajectories leads to weak regular observability in a second order 2D SLAM problem with several possible types of sensors.

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Section: Introductionmentioning

confidence: 61%

“…(2) Range measurements After standard computations from (38), one obtains that, for any (χ, v, ℓ) ∈ R 6 :…”

Section: A Proof Of Proposition 32mentioning

confidence: 99%

Non-uniform Observability for Moving Horizon Estimation and stability with respect to additive perturbation

Flayac¹,

Shames²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Meanwhile, a new class of unmanned vehicles emerged: unmanned water vehicles, which have roles in hydrographic monitoring, facilitate oceanographic research and maritime or river missions. There are two categories of unmanned water vehicles: underwater vehicles and surface vehicles [41]. Both types of vehicles integrate technologies and intelligent devices such as sensors and wireless networks, thus becoming critical components for the IoT landscape.…”

Section: Usv Network Applicationsmentioning

confidence: 99%

Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles

Drăgulinescu

Halunga

Zamfirescu

2021

Sensors

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Currently, the use of unmanned vehicles, such as drones, boats and ships, in monitoring tasks where human presence is difficult or even impossible raises several issues. Continuous efforts to improve the autonomy of such vehicles have not solved all aspects of this issue. In an Internet of Unmanned Vehicles (IoUV) environment, the idea of replacing the static wireless infrastructure and reusing the mobile monitoring nodes in different conditions would converge to a dynamic solution to assure data collection in areas where there is no infrastructure that ensures Internet access. The current paper fills a significant gap, proposing an algorithm that optimises the positions of unmanned vehicles such that an ad hoc network is deployed to serve specific wireless sensor networks that have no other Internet connectivity (hilly/mountainous areas, Danube Delta) and must be connected to an Internet of Things (IoT) ecosystem. The algorithm determines the optimum positions of UV nodes that decrease the path losses below the link budget threshold with minimum UV node displacement compared to their initial coordinates. The algorithm was tested in a rural scenario and 3rd Generation Partnership Project (3GPP), free space and two-ray propagation models. The paper proposes another type of network, a Flying and Surface Ad Hoc Network (FSANET), a concept which implies collaboration and coexistence between unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) and several use cases that motivate the need for such a network.

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“…Different techniques [1,2,3,4,5,6,7] have been proposed for agent relocation to improve the coverage performance of the dynamic system. In addition, the formation control problems are discussed in [8,9,10] where multiple agents provide coverage to targets in an area of interest. Algorithm based approach is proposed in [11], where agents form a structure and adapt it to environmental change.…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimization With Improved Dynamic Performance for Multiagent Systems

2022

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Dynamic coverage is one of the fundamental problems in multi-agent systems (MASs), and is related to optimal placement of nodes to observe a physical space. In a typical coverage problem, a set of targets are required to be monitored. The problem becomes more challenging when the targets are allowed to move as well. Efficient coverage control by mobile agents in a specific area poses many challenges, such as optimal coverage of all targets, dynamic redeployment of agents as targets change their location, trajectory control of each agent during redeployment and determining the number of mobile agents to cover specific targets. In particular, for dynamic coverage problems, agents are deployed to provide coverage to the mobile targets and the agents dynamically redeploy themselves in such a way that they provide maximum coverage to targets not only when they are stationary but also when they are in motion. In many scenarios, such as disaster recovery or public event coverage, dynamic behavior of agents to reach to the next optimal position, plays an important role in determining the performance of the system. In this paper, we propose an augmented Lagrangian based algorithm, which provides a mechanism to control the trajectory of agents to reach the optimal position. By adjusting the gain parameters of the proposed algorithm, we achieve negligible overshoot in response to fast dynamics that is not possible by using conventional Lagrangian. Also, the proposed algorithm is close to the optimal trajectory. Thus, by using the proposed algorithm, we can improve the dynamic performance without compromising the optimal deployment of the agents. The numerical evaluation results show significant improvement in dynamic performance for an example scenario of an MAS.INDEX TERMS Distributed optimization, multi agent dynamical system, optimal coverage problem, optimized transient control, tracking trajectory.

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Multi-Agent Formation Tracking for Autonomous Surface Vehicles

Cited by 44 publications

References 27 publications

Non-uniform Observability for Moving Horizon Estimation and stability with respect to additive perturbation

Non-uniform Observability for Moving Horizon Estimation and stability with respect to additive perturbation

Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles

Distributed Optimization With Improved Dynamic Performance for Multiagent Systems

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