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Multi-robot navigation is a challenging task, especially for many robots, since individual gains may more often than not adversely affect the global gain. This paper investigates the problem of multiple robots moving towards individual goals within a common workspace whereas the motion of every individual robot is deduced by a novel Particle Swarm Optimization (PSO) tuned Feed Forward Neural Network (FFNN). Motion coordination among the robots is implemented using a cooperative coordination algorithm that identifies critical robots and maintains cooperation count while actuating deviation in select robots. The contribution of this paper is twofold; firstly in hybridizing the Artificial Neural Network(ANN) by employing PSO, an evolutionary algorithm, to find optimal values of deviation for every critical robot using velocity and acceleration constraints, secondly ensuing the convergence of the PSO by carrying first and second order stability analysis. Experiments have been carried out to evaluate and validate the efficacy of the proposed coordination schemes by changing the number of robots under hundred different scenarios each, and the founded results demonstrate the efficacy of the proposed schemes. [ABSTRACT FROM AUTHOR]

This paper presents and evaluates artificial neural network (ANN) models used for macrocell path-loss prediction. Measurement data obtained by utilizing the IS-95 pilot signal from a commercial code-division multiple-access (CDMA) mobile network in rural Australia are used to train and evaluate the models. A simple neuron model and feed-forward networks with different numbers of hidden layers and neurons are evaluated regarding their training time, prediction accuracy, and generalization properties. Furthermore, different backpropagation training algorithms, such as gradient descent and Levenberg--Marquardt, are evaluated. The artificial neural network inputs are chosen to be distance to base station, parameters easily obtained from terrain path profiles, land usage, and vegetation type and density near the receiving antenna. The path-loss prediction results obtained by using the ANN models are evaluated against different versions of the semi-terrain based propagation model Recommendation ITU-R P.1546 and the Okumura--Hata model. The statistical analysis shows that a non-complex ANN model performs very well compared with traditional propagation models with regard to prediction accuracy, complexity, and prediction time. The average ANN prediction results were 1) maximum error: 22 dB; 2) mean error: 0 dB; and 3) standard deviation: 7 dB. A multilayered feedforward network trained using the standard backpropagation algorithm was compared with a neuron model trained using the Levenberg--Marquardt algorithm. It was found that the training time decreases from 150 000 to 10 iterations, while the prediction accuracy is maintained. [ABSTRACT FROM AUTHOR]