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For the collaborative control of a team of robots, a set of well-suited high-level control algorithms, especially for path planning and measurement scheduling, is essential. The quality of these control algorithms can be significantly increased by considering uncertainties that arise, e.g. from noisy measurements or system model abstraction, by incorporating stochastic filters into the control. To develop these kinds of algorithms and to prove their effectiveness, obviously real- world experiments with real world uncertainties are mandatory. Therefore, a test-environment for evaluating algorithms for collaborative control of a team of robots is presented. This test-environment is founded on miniature walking robots with six degrees of freedom. Their novel locomotion concept not only allows them to move in a wide variety of different motion patterns far beyond the possibilities of traditionally employed wheel-based robots, but also to handle real-world conditions like uneven ground or small obstacles. These robots are embedded in a modular test-environment, comprising infrastructure and simulation modules as well as a high-level control module with submodules for pose estimation, path planning, and measurement scheduling. The interaction of the individual modules of the introduced test-environment is illustrated by an experiment from the field of cooperative localization with focus on measurement scheduling, where the robots that perform distance measurements are selected based on a novel criterion, the normalized mutual Mahalanobis distance.