Cooperative coverage using receding horizon control

Ahmadzadeh, Ali; Jadbabaie, Ali; Kumar, Vijay; Pappas, George J.

doi:10.23919/ecc.2007.7068965

Cited by 17 publications

(16 citation statements)

References 22 publications

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“…Two main approaches emerge in the literature. One approach uses coverage of an area as the main objective of the surveillance, [18][19][20][21] and another approach focuses on surveillance of areas of highest interest [22,23]. Other approaches combine the two ideas by doing coverage surveillance and also addressing pop-up high interest targets [17,24].…”

Section: Introductionmentioning

confidence: 98%

A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing

et al. 2015

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This paper discusses the development and testing of a flexible genetic algorithm (GA)-based system used for tasking a team of unmanned aerial vehicles (UAVs) to complete a coordinated surveillance mission. The GA development, laboratory testing of the GA to ensure convergence to a \good" solution, integration testing with two ground stations, and the field testing of the algorithms are explained. The algorithm was found to be robust and flexible enough to work in various settings with different UAV types and ground stations.

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Section: Introductionmentioning

confidence: 98%

A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing

et al. 2015

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“…As surveyed above, most of coverage control schemes are based on the gradient flow approach and this paper also takes this approach. To the best of our knowledge, Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, Dec. [9][10][11]2008 ThTA14.6 978-1-4244-3124-3/08/$25.00 ©2008 IEEE only an exception is [8], where receding horizon control strategy is adopted. In addition, instead of using the Voronoi approach where each sensor is assumed to have its own sensing region, we apply the joint detection probability approach motivated by [4].…”

Section: Introductionmentioning

confidence: 99%

“…Here each agent follows a flow, whose inverse would lead the team to an agreement. Coverage control problem based on receding horizon control is considered in [8].…”

Section: Introductionmentioning

confidence: 99%

Coverage control for mobile networks with limited-range anisotropic sensors

Gusrialdi

Hatanaka

Fujita

2008

2008 47th IEEE Conference on Decision and Control

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In this paper the coverage control for mobile sensor networks is studied. The novelty is to consider an anisotropic sensor model where the performance of the sensor depends not only on the distance but also on the orientation from the sensor to the target. Moreover we consider sensors with limited-range sensing defined by a probabilistic model and we assume that each robot is equipped with omni-directional communication capability. A gradient-based distributed algorithm is designed to maximize the joint detection probabilities of the events in the region of interest by the sensors. Simulations illustrate the results.

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“…In Martínez [2005] motion coordination algorithms which maximize the determinant of a Fisher Information Matrix are presented and in Basir [1995] a solution to an active sensing task is given which minimizes the variance of the estimation error and thus reduces the uncertainty of the target state. In addition there are also results like Ahmadzadeh [2007] which are based on receding horizon control or MPC. In this paper the problem of gathering information and monitoring an area with only a few robots is addressed.…”

Section: Introductionmentioning

confidence: 99%

Coverage Control with Information Decay in Dynamic Environments

Hübel

Hirche

Gusrialdi

et al. 2008

IFAC Proceedings Volumes

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In this paper a method for coverage control for a convex region D ⊂ R 2 in a dynamic environment is studied. An information map is introduced in which the information about each point is decaying with respect to time s.t. the robots must revisit them periodically. Also a timevarying density function is used for modeling moving points of interest. The considered gradient based control approach causes the cost function to stay within the desired bounds. But due to the non-stationary problem setup caused by the information decay it does not converge to a single point but to a bounded set, such that the robots keep gathering information continuously. With this method it is possible to gather information about several points of interest within the region D with only a few robots. In the end simulation results are presented to outline the effectiveness of the proposed control law.

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Cooperative coverage using receding horizon control

Cited by 17 publications

References 22 publications

A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing

A Flexible Genetic Algorithm System for Multi-UAV Surveillance: Algorithm and Flight Testing

Coverage control for mobile networks with limited-range anisotropic sensors

Coverage Control with Information Decay in Dynamic Environments

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