In this paper, we deal with the problem of distributed target tracking with heterogeneous mobile robots. We extend the static sensor networks framework, presented by some of the authors in [1], introducing a cooperative control algorithm for path planning. In particular, each mobile node has limited sensing range and can estimate (either by measuring or by predicting) the target position. Then, a totally distributed algorithm exploits a suitable max-consensus protocol to reach a global agreement on the best estimate available in the network at the given time instant. It is based on the spread of a perception confidence value, locally computed by every single node and related to the Fisher Information, and requires a connected network to converge. Finally, a control law based on the artificial potential fields method makes the agents move in order to improve the algorithm performance. In particular, the distributed motion control algorithm is capable of trading off among the following requirements: 1) approaching the target to improve the accuracy of the measurement, 2) performing collision avoidance with other agents and the target, 3) maintaining the network connectivity, 4) not getting too much far from the initial position, in order not to worsen too much the coverage of the sensed area. Extensive simulation results are provided to confirm the suitability of the approach.

Coverage-aware distributed target tracking for mobile sensor networks

Silvia Giannini;Donato Di Paola;Alessandro Rizzo
2012

Abstract

In this paper, we deal with the problem of distributed target tracking with heterogeneous mobile robots. We extend the static sensor networks framework, presented by some of the authors in [1], introducing a cooperative control algorithm for path planning. In particular, each mobile node has limited sensing range and can estimate (either by measuring or by predicting) the target position. Then, a totally distributed algorithm exploits a suitable max-consensus protocol to reach a global agreement on the best estimate available in the network at the given time instant. It is based on the spread of a perception confidence value, locally computed by every single node and related to the Fisher Information, and requires a connected network to converge. Finally, a control law based on the artificial potential fields method makes the agents move in order to improve the algorithm performance. In particular, the distributed motion control algorithm is capable of trading off among the following requirements: 1) approaching the target to improve the accuracy of the measurement, 2) performing collision avoidance with other agents and the target, 3) maintaining the network connectivity, 4) not getting too much far from the initial position, in order not to worsen too much the coverage of the sensed area. Extensive simulation results are provided to confirm the suitability of the approach.
51st IEEE Annual Conference on Decision and Control, CDC 2012
978-1-4673-2065-8
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/16944
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