A connected network of robots, sensors, and smart devices has the potential to solve grand challenges in domains such as agronomy, oceanography, and emergency response. Robots will form the "physical" layer of this Internet-of-Things and collect data from hard to reach places at unprecedented spatio-temporal scales. Heterogeneity in sensing and mobility in these robot teams is critical in order for us to effectively collect data from diverse, unstructured, natural environments. 

In this talk, I will present our recent work on devising efficient algorithms for data collection with heterogeneous robot teams.

 We will use tools from computational geometry, combinatorial optimization, stochastic optimization, Bayesian neural networks, and information theory to design these algorithms. I will present our experiments results on bridge inspection with aerial robots, precision agriculture with aerial and ground robots, monitoring marine environments with aerial robots and robotic boats. I will conclude by discussing our recent efforts that aim to bridge the gap between algorithmic and field robotics.