This work describes the application of a compact, MEMS-based, 2D anemometer to the estimation of a quadrotor's airspeed. Correcting for the vehicle's ground speed provided by internal GPS and inertial units allows this low cost, mobile platform to provide local wind speed estimates. A series of initial, bench-top tests were performed to characterize and calibrate the sensor, which is an improved version of a recently proposed and novel device. Additional full-scale wind tunnel experiments were performed with the sensor mounted on a fixed quadrotor to test the effect of the propellers on the sensor's performance

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Autonomous navigation through unknown environments is a challenging task that entails real-time localization, perception, planning, and control. UAVs with this capability have begun to emerge in the literature with advances in lightweight sensing and computing. Although the planning methodologies vary from platform to platform, many algorithms adopt a hierarchical planning architecture where a slow, low-fidelity global planner guides a fast, high-fidelity local planner. However, in unknown environments, this approach can lead to erratic or unstable behavior due to the interaction between the global planner, whose solution is changing constantly, and the local planner; a consequence of not capturing higher-order dynamics in the global plan. This work proposes a planning framework in which multi-fidelity models are used to reduce the discrepancy between the local and global planner. Our approach uses high-, medium-, and low-fidelity models to compose a path that captures higher-order dynamics while remaining computationally tractable. In addition, we address the interaction between a fast planner and a slower mapper by considering the sensor data not yet fused into the map during the collision check. This novel mapping and planning framework for agile flights is validated in simulation and hardware experiments, showing replanning times of 5-40 ms in cluttered environments.

Abstract-Although unmanned air vehicles' increasing agility and autonomy may soon allow for flight in urban environments, the impact of complex urban wind fields on vehicle flight performance remains unclear. Unlike synoptic winds at high altitudes, urban wind fields are subject to turbulence generated by the buildings and terrain. The resulting spatial and temporal variation makes inference about the global wind field based on local wind measurements difficult and prevents the use of most simple wind models. Fortunately, the structure of the urban environment provides exploitable predictability given a suitable computational fluid dynamics solver, a representative 3D model of the environment, and an estimate of the expected prevailing wind speed and heading. The prevailing wind speed and direction at altitude and computational fluid dynamics solver can generate the corresponding wind field estimate over the map. By generating wind fields in this way, this work investigates a quadrotor's ability to exploit them for improved flight performance. Along with the wind field estimate, an empirically derived power consumption model is used to find minimum-energy trajectories with a planner both aware of and naive to the wind field. When compared to minimum-energy trajectories that do not incorporate wind conditions, the wind-aware trajectories demonstrate reduced flight times, total energy expenditures, and failures due to excess air speed for trajectories across MIT campus.

We present a motion planning algorithm for dynamic vehicles navigating through unknown environments. We focus on the scenario in which a fast-moving car attempts to navigate from a start location to a set of goal coordinates in minimum time with no prior information about the environment, building a map in real time from onboard sensor data. Whereas existing planners for exploration confine themselves to a conservative set of constraints to guarantee safety around unknown regions of the environment, we instead learn a hazard function from data, which maps the vehicle's dynamic state and current environment knowledge to a probability of collision. We perform receding horizon planning in which the objective function is evaluated in expectation over those learned probabilities of collision. Our algorithm demonstrates sensible emergent behaviors, like swinging wide around blind corners, slowing down near the map frontier, and accelerating in regions of high visibility. Our algorithm is capable of navigating from start to goal much more quickly than the conservative baseline planner without sacrificing safety. We demonstrate our algorithm on a 1:8-scale high-performance RC car equipped with a planar laser range-finder and inertial measurement unit, reaching speeds of 4m/s in unknown, indoor spaces. A video of experimental results is available at: http: //groups.csail.mit.edu/rrg/nav_learned_prob_collision.