Abstract: The prediction of the trajectory of mobile objects is important in many robotics applications like robot motion planning and collision avoidance. In most cases, the measurements, on which predictions are based, are subject to noise and errors. This paper presents a neural network based approach for the prediction of short-term motions of mobile objects. We studied the effect of white additive noise and gaussian noise on the prediction accuracy. An adaptive continued learning strategy is used to reduce the prediction error and accurately track the mobile objects. An empirical study was conducted to determine the architectural features of the network (number of layers and number of neurons in each layer) and the learning parameters (learning rate, momentum factor and convergence criterion) that minimize the mean squared prediction error giving an acceptable time response. The mean squared error, and the average time performance of the network (number of learning steps before convergence) are used as performance criteria. The network results are compared with those obtained from a linear regression algorithm. The neural network outperformed the linear regression in accurately predicting swiftly changing motion patterns.!9
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