Cooperative Robots and Sensor Networks 2014 by Anis Koubaa, Abdelmajid Khelil

By Anis Koubaa, Abdelmajid Khelil

This ebook is the second one quantity on Cooperative Robots and Sensor Networks. the first aim of this publication is to supply an updated reference for state of the art experiences and examine tendencies with regards to cellular robots and instant sensor networks, and specifically for the coupling among them.

Indeed, cellular robots and instant sensor networks have enabled nice potentials and a wide house for ubiquitous and pervasive functions. Robotics and instant sensor networks have in most cases been regarded as separate study fields and little paintings has investigated the wedding among those applied sciences. notwithstanding, those applied sciences proportion a number of beneficial properties, let universal cyber-physical purposes and supply complementary help to one another. The ebook comprises ten chapters, equipped into 4 parts.

The first a part of the publication offers 3 chapters with regards to localization of cellular robots utilizing instant sensor networks. chapters provided new ideas established prolonged Kalman clear out and Particle filter out for localizing the robots utilizing variety measurements with the sensor community. The 3rd bankruptcy provides a survey on mobility-assisted localization ideas in instant sensor networks.

The moment a part of the ebook bargains with cooperative robots and sensor networks purposes. One bankruptcy provides a accomplished assessment of significant functions coupling among robots and sensor networks and gives real-world examples in their cooperation. different chapters current purposes for underwater robots and sensor networks.

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Extra resources for Cooperative Robots and Sensor Networks 2014

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XN yN ]T (3) The proposed on-line EIF SLAM algorithm is described in Algorithms 1 and 2. EIF, like other Bayesian filters, is applied in two main stages: prediction and correction. The prediction stage is carried out by the motion update function (see Algorithm 1) and the correction stage, by the measurement update function (see Algorithm 2). 2. The operation of EIF is a prediction-correction loop, like EKF. But here, because of the use of the information form, there is a third step in the loop that recovers μt , the estimate of the state vector, using the expression: μt = Ωt−1 ξt (4) Thus, it is necessary to perform the following steps: 1.

ToA measurement requires knowing the time when the signal was transmitted, thus, most of the time, synchronization between sender and receiver is needed.

Xt (9) [m] Each particle xt (with 1 ≤ m ≤ M) is a sample of the state at the time instant t, that is, an hypothesis of the state vector at time t. Here M denotes the number of particles in the set Xt . g. M = 500), although in some implementations M is adapted dynamically. Like other Bayes filters, PF performs a recursive estimation based on the last estimation made at the previous time instant. Thus, PF builds a new set of particles Xt recursively based on the set Xt−1 . It has three main steps: motion estimation, importance sampling and importance resampling.

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