This paper presents an experiment in collective robotics which investigates the influence of communication, of learning and of the number of robots in a specific task, namely learning the topography of an environment whose features change frequently. We propose a theoretical framework based on probabilistic modeling to describe the system's dynamics. The adaptive multi-robot system and its dynamic environment are modeled through a set of probabilistic equations which give an explicit description of the influence of the different variables of the system on the data-collecting performance of the group. Further, we implement the multi-robot system in experiments with a group of Khepera robots and in simulation using Webots, a three-dimensional simulator of Khepera robots. The robots are controlled by a distributed architecture with an associative-memory type of learning algorithm. Results show that the algorithm allows a group of robots to keep an up-to-date account of the environmental state when this changes regularly. Finally, the results of the simulated and physical experiments are compared with the predictions of the probabilistic model. It is found that the model shows both a good qualitative and a good quantitative correspondence to these results. This suggests that a probabilistic model can be a good first approximation of a multi-robot system.