2013
DOI: 10.1016/j.automatica.2012.09.012
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Spatially distributed area coverage optimisation in mobile robotic networks with arbitrary convex anisotropic patterns

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Cited by 41 publications
(19 citation statements)
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“…In [5] a different scheme was presented in case of range-limited, uniform sensing patterns that locally maximizes the total area covered by the network. Many research activities extended these works for networks with either heterogeneous [6][7][8] or anisotropic [9][10][11] sensing capabilities. Another extension of [4] is presented in [12,13] where RF communication constraints are imposed on the sensor network.…”
Section: Introductionmentioning
confidence: 99%
“…In [5] a different scheme was presented in case of range-limited, uniform sensing patterns that locally maximizes the total area covered by the network. Many research activities extended these works for networks with either heterogeneous [6][7][8] or anisotropic [9][10][11] sensing capabilities. Another extension of [4] is presented in [12,13] where RF communication constraints are imposed on the sensor network.…”
Section: Introductionmentioning
confidence: 99%
“…Area coverage problem has been tackled for years using either one single robot [2] [3] or multi-robots/swarm robots [4] [5]. It is also indispensable in simultaneous localization and mapping.…”
Section: Introductionmentioning
confidence: 99%
“…a commonly used 4,35,36 simplified version of the Dubin's car model that incorporates both the position and the orientation of a robot into the robot dynamics.…”
Section: Problem Statementmentioning
confidence: 99%
“…1 Tasks such as area coverage (exploration), [2][3][4][5] surveillance, 6 search and rescue missions require that the robots move efficiently in the environment, avoiding obstacles during motion and keeping under consideration the robots' physical constraints. The majority of research on motion planning in the past few decades focused on known static environments, 7 relying on principles such as the artificial potential fields, 8 the vector field histogram, 9 probabilistic roadmaps 10 and rapidly exploring random trees (RRT).…”
Section: Introductionmentioning
confidence: 99%