CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model
This work addresses flight safety and risk assessment for multirotor UAVs, with potential applications in improving operational safety and coverage evaluation, though it is incremental as it builds on existing energy modeling and risk metrics.
The paper tackles the problem of predicting energy consumption and assessing battery depletion risk for UAV flights by developing a deep energy model using Temporal Convolutional Networks, achieving a 29% improvement in power predictions over a state-of-the-art analytical method, and proposes using Conditional Value-at-Risk (CVaR) to quantify flight risk based on worst-case energy scenarios.
Energy management is a critical aspect of risk assessment for Uncrewed Aerial Vehicle (UAV) flights, as a depleted battery during a flight brings almost guaranteed vehicle damage and a high risk of human injuries or property damage. Predicting the amount of energy a flight will consume is challenging as routing, weather, obstacles, and other factors affect the overall consumption. We develop a deep energy model for a UAV that uses Temporal Convolutional Networks to capture the time varying features while incorporating static contextual information. Our energy model is trained on a real world dataset and does not require segregating flights into regimes. We illustrate an improvement in power predictions by $29\%$ on test flights when compared to a state-of-the-art analytical method. Using the energy model, we can predict the energy usage for a given trajectory and evaluate the risk of running out of battery during flight. We propose using Conditional Value-at-Risk (CVaR) as a metric for quantifying this risk. We show that CVaR captures the risk associated with worst-case energy consumption on a nominal path by transforming the output distribution of Monte Carlo forward simulations into a risk space. Computing the CVaR on the risk-space distribution provides a metric that can evaluate the overall risk of a flight before take-off. Our energy model and risk evaluation method can improve flight safety and evaluate the coverage area from a proposed takeoff location. The video and codebase are available at https://youtu.be/PHXGigqilOA and https://git.io/cvar-risk .