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In this paper we deal with the problem of covering an environment using a group of mobile robots with nonholonomic kinematic and dynamic constraints. In comparison with standard coverage control procedures, we develop a combined controller for Voronoi-based coverage approach in which kinematic and dynamic constraints of the actual mobile sensing robots are incorporated in the controller design. The...
This study proposes a distributed control algorithm for coverage in unknown environments with networked mobile sensors. The control law is derived according to the online neural network estimation of the sensory function of the environments. In contrary to previous adaptive approaches, a major advantage of the proposed method is that due to the neural network nonlinear approximation property, it is...
This study considers a functional link neural network (FLNN) structure for identifying nonlinear dynamic systems. We tackle the problem of system identification in noisy environments by introducing an adaptive tuning structure based on individual particle optimization (IPO) for the nonlinear systems identification via functional link neural network. The IPO algorithm is applied in order to train the...
This paper proposes a novel deployment algorithm for wireless sensor networks namely VFIPO based on individual particle optimization (IPO) incorporated with virtual force (VF) algorithm, which overcomes the shortcomings of applying the VF algorithm. Simulation results verify that mobile nodes deployment with VFIPO outperforms the previous deployment algorithms such as VF, PSO, VFPSO and IPO based...
Effective sensor coverage is one of the key topics addressed in wireless sensor networks (WSNs) study, which refers to the deployment and detection probability of WSNs. This paper proposes a novel deployment algorithm for mobile sensor networks, based on individual particle optimization (IPO). The algorithm is designed for real-time online deployment for the purpose of maximum coverage in the environment...
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