Abstract-Solar-powered Unmanned Aerial Vehicles (SUAV) designed for Low-Altitude Long-Endurance (LALE) applications provide potential multi-day continuous flight capability, but are generally prone to local meteorological impediments such as rain, strong winds or reduced solar irradiance. This paper therefore presents METPASS, the Meteorology-aware Trajectory Planning and Analysis Software for Solar-powered UAVs. METPASS optimizes largescale solar-powered UAV missions using a detailed consideration of meteorological effects: An optimal trajectory is found on a 3-D grid for given departure and arrival points by applying a Dynamic Programming approach and a cost function that considers environmental hazards, winds, solar radiation, aircraft parameters and flight time. The cost function is evaluated based on a kinematic and energetic UAV system model and forecast data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The trajectoryplanning environment is applied to an envisioned fully autonomous and solar-powered crossing of the North Atlantic Ocean by AtlantikSolar, a 5.6m-wingspan SUAV developed at ETH Zurich. Results based on historical ECMWF weather data from 2012 and 2013 show that properly pre-optimized routes allow the Atlantic crossing even in case of significant global cloud coverage and that optimal routes can reduce the required flight time by up to 50% (from 106h to 52h) by exploiting wind conditions.

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Solar-powered aircraft promise significantly increased flight endurance over conventional aircraft. While this makes them promising candidates for large-scale aerial inspection missions, their structural fragility necessitates that adverse weather is avoided using appropriate path planning methods. This paper therefore presents MetPASS, the Meteorology-aware Path Planning and Analysis Software for Solar-powered UAVs. MetPASS is the first path planning framework in the literature that considers all aspects that influence the safety or performance of solar-powered flight: It avoids environmental risks (thunderstorms, rain, wind, wind gusts and humidity) and exploits advantageous regions (high sun radiation or tailwind). It also avoids system risks such as low battery state of charge and returns safe paths through cluttered terrain. MetPASS imports weather data from global meteorological models, propagates the aircraft state through an energetic system model, and then combines both into a cost function. A combination of dynamic programming techniques and an A*-search-algorithm with a custom heuristic is leveraged to plan globally optimal paths in station-keeping, point-to-point or multi-goal aerial inspection missions with coverage guarantees. A full software implementation including a GUI is provided. The planning methods are verified using three missions of ETH Zurich's AtlantikSolar UAV: An 81-hour continuous solar-powered station-keeping flight, a 4000 km Atlantic crossing from Newfoundland to Portugal, and two multi-glacier aerial inspection missions above the Arctic Ocean performed near Greenland in summer 2017. It is shown that integrating meteorological data has significant advantages and is indispensable for the reliable execution of large-scale solar-powered aircraft missions. For example, the correct selection of launch date and flight path across the Atlantic Ocean decreases the required flight time from 106 hours to only 52 hours.

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