Mission control - combined solutions for source seeking and level curve tracking in a time-varying field

Turgeman, Avi; Werner, Herbert

doi:10.23919/acc.2017.7963611

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…Their robot controller is analytically constructed with proved convergence for chemicals or liquid substances poured in a marine environment. The method of Turgeman and Werner (2017) allocates robots to track the boundary and to find the plume source simultaneously. In this type of scenario, there is an interest in deploying robust robots in real environments (Mellucci et al 2017).…”

Section: Tracking Static and Dynamic Boundariesmentioning

confidence: 99%

Estimating boundary dynamics using robotic sensor networks with pointwise measurements

et al. 2021

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In this paper, we consider environmental boundaries that can be represented by a time-varying closed curve. We use n robots equipped with location sensors to sample the dynamic boundary. The main difficulty during the prediction process is that only n boundary points can be observed at each time step. Our approach combines finite Fourier series for shape-estimation and polynomial fitting for point tracking in time. This combination gives a continuous parametric function that describes the boundary shape and its dynamics. We validate our strategy in simulation and with experiments using actual robots. We tested on non-convex boundaries assuming noisy measurements and inaccurate motion actuators.

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Section: Tracking Static and Dynamic Boundariesmentioning

confidence: 99%

Estimating boundary dynamics using robotic sensor networks with pointwise measurements

et al. 2021

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“…Similar to the source seeking problem, the level curve tracking problem generally depends on estimating the field gradient which requires sharing of field measurements. In [39] and [40], they designed mission control laws that achieve source seeking and level curve tracking. The mission control laws are based on estimating the gradient and communicating measurements as well as maintaining specific formations.…”

Section: Multi-agent Level Curve Trackingmentioning

confidence: 99%

“…In [71,72] the field gradient is assumed to be known and then a control law is designed such that the agents move perpendicular to the gradient. Alternatively, the algorithms in [39] and [40] rely on communicating field measurements and maintaining prescribed formations to estimate the field gradient. Independent of gradient estimation, an algorithm is designed in [41] for a 2-agent system, but, it requires communicating field measurements.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer Swarm Control Model for Information Propagation and Multi-Tasking

Al-Abri

Maxon

Zhang

2019

2019 American Control Conference (ACC)

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To my great wife Zainab and my brilliant children Ibrahim and Lateen without whom this work would have been impossible. I also give my deep thanks to Prof. Fumin Zhang, my advisor, for his support, guidance, and encouragement during my Ph.D. journey. In particular, he taught me how to aim higher and what really matters to be a successful researcher. I would like to thank my fellow doctoral students for their feedback, cooperation, and help. In particular, I want to thank my colleague Sean Maxon for all the brainstorming and discussions we have together. I also want to thank my colleagues Tony Lin, Mengxue Hou and Qiuyang Tao who helped me running blimp experiments at the Georgia Tech Systems Research Lab, and Sean Wilson who helped me running experiments at Georgia Tech Robotarium.Finally, I would like to thank my thesis committee members: Prof. Yorai Wardi, Prof.

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“…In the literature, previous efforts to solve the dual problems of source seeking and level-curve tracking have relied on field gradient and Hessian estimation [6], [8], [17], [35], [40], [41], [43], [47], extremum seeking control [12], [13], [25], [28], sliding-mode control [32], [38] , and weighted consensus laws [16]. Most of the aforementioned control strategies rely either on sharing measurements via communication channels, requiring specific spatial formations, or apply only to certain sizes and structures of interacting graphs.…”

Section: Introductionmentioning

confidence: 99%

“…For the level curve tracking, in [47] and [43], the field gradient is assumed to be known. Alternatively, the algorithms in [41] and [6] rely on communicating field measurements and maintaining prescribed formations to estimate the field gradient. In [42], a cooperative control law is designed for two agents such that one agent estimates the field gradient and the other one tracks the plume front.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Active Perception Strategy for Source Seeking and Level Curve Tracking

Al-Abri

Zhang

2022

IEEE Trans. Automat. Contr.

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Algorithms for multi-agent systems to locate a source or to follow a desired level curve of spatially distributed scalar fields generally require sharing field measurements among the agents for gradient estimation. Yet, in this paper, we propose a distributed active perception strategy that enables swarms of various sizes and graph structures to perform source seeking and level curve tracking without the need to explicitly estimate the field gradient or explicitly share measurements. The proposed method utilizes a consensus-like Principal Component Analysis perception algorithm that does not require explicit communication in order to compute a local body frame. This body frame is used to design a distributed control law where each agent modulates its motion based only on its instantaneous field measurement. Several stability results are obtained within a singular perturbation framework which justifies the convergence and robustness of the strategy. Additionally, efficiency is validated through various computer simulations and robots implementation in 2-D scalar fields. The active perception strategy leverages the available local information and has the potential to be used in various applications such as modeling information propagation in biological and robotic swarms.

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Mission control - combined solutions for source seeking and level curve tracking in a time-varying field

Cited by 11 publications

References 20 publications

Estimating boundary dynamics using robotic sensor networks with pointwise measurements

Estimating boundary dynamics using robotic sensor networks with pointwise measurements

A Multi-Layer Swarm Control Model for Information Propagation and Multi-Tasking

A Distributed Active Perception Strategy for Source Seeking and Level Curve Tracking

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