# CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model

arXiv: https://arxiv.org/abs/2105.15189 · HTML: https://mlsanity.com/paper/2105.15189/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 6 of 6)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics](https://mlsanity.com/paper/1710.11040/) (2017)
- [Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs](https://mlsanity.com/paper/1405.5848/) (2014)
- [Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles](https://mlsanity.com/paper/2003.11675/) (2020)
- [In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery](https://mlsanity.com/paper/2103.13313/) (2021)

## Built on by (most cited first, 6 of 6)

- [On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes](https://mlsanity.com/paper/2304.12477/) (2023)
- [Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation](https://mlsanity.com/paper/2302.00134/) (2023)
- [Learning Risk-aware Costmaps for Traversability in Challenging Environments](https://mlsanity.com/paper/2107.11722/) (2021)
- [Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers](https://mlsanity.com/paper/2205.00562/) (2022)
- [Robot Navigation in Risky, Crowded Environments: Understanding Human Preferences](https://mlsanity.com/paper/2303.08284/) (2023)
- [Data-Efficient Modeling for Precise Power Consumption Estimation of Quadrotor Operations Using Ensemble Learning](https://mlsanity.com/paper/2205.10997/) (2022)

## Abstract (arXiv)

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 .