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Persistent coverage aims to maintain a certain coverage level over time in an environment where such level deteriorates. This level can be associated to temperature, dust or sensor information. We propose an algorithmic solution in which each robot locally finds the best paths and coverage actions to keep the desired coverage level over the whole environment. Using Fast Marching Methods, optimal paths...
Sensor networks must be able to fuse nodes’ perceptions in a reliable way to reach a trustworthy consensus. Data association mistakes and measurement outliers are some of the factors that can contribute to incorrect perceptions and considerably affect consensus values. In this paper, we present a novel distributed scheme for robust consensus in autonomous sensor networks. The proposed method builds...
This paper presents a novel method that enables a team of aerial robots to enclose a target in 3D space by attaining a desired geometric formation around it. We propose an approach in which each robot obtains its motion commands using measurements of the relative position of the other agents and of the target, without the need for a central coordinator. As contribution, our method permits any desired...
We study the problem of distributed coverage with connectivity maintenance for robot networks with range-limited communication and sensing.We let the robots perform coverage while keeping a set of communication links. The structure that ensures that the network does not get disconnected, and that provides the highest freedom of robot motions is the Minimum-distance Spanning Tree (MST). As robots move...
Sensor fusion methods combine noisy measurements of common variables observed by several sensors, typically by averaging information matrices and vectors of the measurements. Some sensors may have also observed exclusive variables on their own. Examples include robots exploring different areas or cameras observing different parts of the scene in map merging or multi-target tracking scenarios. Iteratively...
Data association mistakes, estimation and measurement errors are some of the factors that can contribute to incorrect observations in robotic sensor networks. In order to act reliably, a robotic network must be able to fuse and correct its perception of the world by discarding any outlier information. This is a difficult task if the network is to be deployed remotely and the robots do not have access...
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