Recent advances in miniaturization of processing units, storage capacity, battery power and sensory equipment have allowed Unmanned Aerial Vehicles (UAVs) to perform environmental monitoring tasks with unprecedented speed and accuracy. Data collection is important for algorithms and systems that try to learn how the physical world works or try to interact with it. The number, variety and quality of the data directly affects the performance of these algorithms. In order to fully realize this vision we need to compliment it with efficient systems that can collect the required data. In this dissertation we develop new robotic solutions for fully automating monitoring and data collection in natural, outdoor environments. First, we study the design of an Unmanned Aerial Vehicle (UAV) for safe tree surface inspection flying at low altitude inside orchard type fields. The objective of this study is threefold. The system needs to collect complete sets of data for different types of data collection sensors. Furthermore, it has to be able to operate successfully under the effects of wind disturbances. Finally, the integrity of the field has to be guaranteed. To achieve this goal, we modify and integrate several methods and technologies including a non-standard distance-velocity Proportional-Integral-Derivative (PID) based controller and real time obstacle map navigation based on occupancy voxels. The resulting system demonstrates successful operation and data collection inside a honeycrisp apple orchard. The demonstration includes multiple tests across several days, under various weather conditions (e.g. sunlight, wind) to ensure consistency and was shown to be fully functional even during GPS signal loss. Second, we study the problem of high altitude optimal trajectory generation for capturing aerial image footage of known but difficult to see areas (e.g. under trees or structures, reflective surfaces). In this problem we consider the relation between the camera resolution and UAV altitude. We associate each camera image with an inverted cone apexed at the location of the interest. The height of each cone is associated with the desired resolution and the apex angle corresponds to camera field of view. In other words, each cone encodes the set of view points from which a target can be imaged at a desired location. We provide a polynomial time approximation algorithm that produces a close to optimal solution and was evaluated in existing applications. We analyze the performance of our strategy and demonstrate through simulations and field experiments that by exploiting the special structure of the cones we can achieve shorter flight times than previously available solutions. The strategy can be used with any number of cones and split coverage into multiple flights in order to account for limited battery power or storage capacity. Third, we describe a method that can localize and approach a radio signal source at an unknown location with UAVs. We start by fitting a multi-rotor UAV system with a small on-board computer and a directional antenna that can detect the signal source. We then model the area around the signal source based on the antenna radiation field and classify the locations in which we can or cannot obtain reliable directionality measurements (i.e. bearing measurements). The results of this modeling resemble a cone-like region above the signal source inside of which bearing measurements are unreliable. In order to verify that our modeling is realistic, we also collect data with a real UAV system. Using this modeling, we develop a “home-in” strategy that takes advantage of a UAV’s ability to change altitude and exploits the special structure of the modeled conic-like region in order to approach the signal source from above. We analyze the performance of our strategy and demonstrate through simulations and field experiments that by exploiting this structure we can achieve short flight times. In this dissertation we make progress towards the creation of robotic sensing solutions that satisfy two important criteria. The first criterion is to provide theoretical guarantees about the performance of the proposed solutions. This is achieved by mathematically proving what the worst case scenario is and using it as an upper bound. The second criterion is to demonstrate the feasibility of the proposed solutions in real world applications. This is achieved by providing practical implementations tested in both simulations and with robotic systems operating in realistic settings.