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We perform analysis of packet end-to-end delay in order to explain the processes taking place in the network. We build statistical models to describe the packets end-to-end delay distribution. Our experiments show that packet delay in an ad-hoc network of mobile robots depends on the number of hops traveled by the packet. We intend to use this dependency when implementing a modification of the learning model of ant routing algorithms addressed to improve their performance in such demanding environments.