In many sensing applications, including environmental monitoring, measurement systems must cover a large space with only limited sensing resources. One approach to achieve required sensing coverage is to use robots to convey sensors within this space. Planning the motion of these robots -- coordinating their paths in order to maximize the amount of information collected while placing bounds on their resources (e.g., path length or energy capacity) -- is a NP-hard problem. In this poster, we present an efficient path planning algorithm that coordinates multiple robots, each having a resource constraint, to maximize the “informativeness" of their visited locations. In particular, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to characterize the amount of information collected. We provide strong theoretical approximation guarantees for our algorithm by exploiting the sub-modularity property of mutual information. We provide an empirical analysis of our algorithm from field experiments, using Networked Info Mechanical Systems (NIMS) family of robotic systems. The NIMS family of sensing systems, together with an efficient experimental design approach that involves phenomena modeling, enabled the first high resolution imaging of several important scientific phenomena such as contaminant concentration and algal bloom dynamics. This work is currently being applied to survey entire river systems in interdisciplinary investigations providing scientists with important new characterization of primary national water resources.