Overcoming Traditional Assessment Limitations with Probabilistic Modeling for Mission Risk Calculation
Solar-powered unmanned aerial vehicles (UAVs) are increasingly recognized as a vital tool for long-duration missions, including environmental monitoring, precision agriculture, and search-and-rescue operations. However, their endurance is highly susceptible to weather variability, particularly in regions like Central Europe, where dynamic low-altitude cloud cover can rapidly alter solar energy availability. Traditional assessment methods, which rely on long-term averages or typical meteorological year data, have been found to produce errors exceeding 40%, rendering them inadequate for predicting real-world mission performance with precision.
To address this critical gap, a research team led by Piotr Lichota at the Faculty of Power and Aeronautical Engineering at Warsaw University of Technology has developed the world’s first mission feasibility assessment model for solar-powered UAVs. The team utilized SolarAnywhere’s comprehensive 25-year satellite-derived solar irradiance dataset, integrating it with a Maximum Likelihood Estimation algorithm to reconstruct high-precision direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) profiles. This approach has reduced errors by over 30% compared to existing standards. Additionally, the model introduces a cloud cover simulation component, enabling the assessment of real-world atmospheric fluctuations and their impact on UAV power generation efficiency.
Lichota emphasized that the conventional "clear-sky assumption" is entirely inadequate in regions prone to frequent cloud cover. The new model, however, allows for the calculation of probability curves for achieving target flight durations based on specific times, locations, and flight configurations. For instance, the research revealed that a UAV launched at noon on a given day could achieve an 85% success rate for a six-hour mission, whereas the same UAV launched at 8 AM under identical conditions would only have a 45% success rate. This disparity is primarily attributed to variations in cloud cover and solar altitude angles. These findings challenge the long-standing reliance on theoretical maximum endurance calculations and provide UAV manufacturers with a more accurate tool for risk assessment and mission planning.
Risk-Oriented Design to Accelerate Commercialization of Green Technology
Funded by the National Science Centre, Poland (grant number 2025/09/X/ST8/00294), this research marks the first integration of risk management principles into the design of solar-powered UAVs. The team’s simulations demonstrated that mission success rates could be significantly improved by adjusting key parameters such as the ratio of solar panel area to wing area, battery capacity, and even the selection of takeoff times. For example, increasing battery capacity from 100Wh to 150Wh was shown to elevate the success rate of a four-hour mission from 60% to 90%, though this improvement must be balanced against the trade-offs in airframe weight and cost.
Lichota highlighted that the assessment framework is not limited to solar-powered UAVs but can also be extended to other solar-dependent green technologies, such as high-altitude pseudo-satellites (HAPS) and solar-powered maritime vessels. The research team is currently collaborating with Polish search-and-rescue organizations to test the model’s applicability in real-world scenarios, including mountain and maritime search-and-rescue missions. Preliminary results from these field tests are expected to be published in early 2027. The research provides a robust scientific foundation for the commercialization of green technologies, mitigating the risks of mission failure due to over-reliance on theoretical assumptions and advancing the practical deployment of UAVs in environmental monitoring and emergency response.
