2009
DOI: 10.1007/978-3-642-00312-7_6
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Simultaneous Coverage and Tracking (SCAT) of Moving Targets with Robot Networks

Abstract: We address the problem of simultaneously covering an environment and tracking intruders (SCAT). The problem is translated to the task of covering environments with time-varying density functions under the locational optimization framework. This allows for coupling the basic subtasks: task assignment, coverage, and tracking. A decentralized controller with guaranteed exponential convergence is devised. The SCAT algorithm is verified in simulations and on a team of robots.

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Cited by 91 publications
(63 citation statements)
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“…Due to the dynamic nature of I(q, P, t), control and stability analysis are inherently more difficult. Note that contrary to [5], the density function here (mutual information) is also dependent on the robot group configuration. I(q, P, t)…”
Section: Objective and Assumptionsmentioning
confidence: 92%
See 1 more Smart Citation
“…Due to the dynamic nature of I(q, P, t), control and stability analysis are inherently more difficult. Note that contrary to [5], the density function here (mutual information) is also dependent on the robot group configuration. I(q, P, t)…”
Section: Objective and Assumptionsmentioning
confidence: 92%
“…The authors of [5] extend the coverage algorithm of [3] to account for tracking of moving targets. Their approach involves time-varying coverage functions -similarly to this paper-but the stability analysis follows a completely different route, since it does not involve information theory.…”
Section: Sensor Network Deploymentmentioning
confidence: 99%
“…Constructing approximation (24) requires that robot i 1 move d times around x i1 [k], to the positions x i1 [k]+δe l , l = 1, . .…”
Section: A Two-time-scale Spsa Algorithmmentioning
confidence: 99%
“…As described in Section II, we consider generic communication-constrained deployment problems, where the goal is to move a mobile robotic network from an initial configuration to a desired steady-state configuration appropriate for a given task, without directly attempting to optimize its transient trajectory. This framework has a wide range of applications in robotics [20], including 0733-8716/12/$31.00 c 2012 IEEE formation control [21], coverage control [22], dynamic vehicle routing [23], or target tracking [24], and can also be used to continuously adapt the configuration in slowly varying environments. One of its main advantages is that it typically leads to distributed algorithms, which are more amenable to implementation in large networks.…”
mentioning
confidence: 99%
“…The behaviour of bacteria was used by Mesquita et al [5] to develop a controller capable of providing coverage of a spatiotemporal distribution without taking collisions into account. However, from all the above mentioned approaches, the use of the Voronoi partition technique has gained a lot of popularity and has been used in various forms as in [6] [7][8] [9] [10].…”
Section: Introductionmentioning
confidence: 99%