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

• URL: http://arxiv.org/abs/2105.15189v1
• Date: Mon, 31 May 2021 17:51:55 GMT
• Title: CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model
• Abstract summary: Energy management is a critical aspect of risk assessment for Uncrewed Aerial Vehicle (UAV) flights. We develop a deep energy model for a UAV that uses Temporal Convolutional Networks to capture the time varying features. Conditional Value-at-Risk (CVaR) captures the risk associated with worst-case energy consumption on a nominal path.
• Score: 1.8593061465167362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 .

Related papers

• Energy-Aware Wind-Resilient Routing for Truck-Assisted Multi-UAV Delivery under Wind Uncertainty [8.00361595836773]
  Energy feasibility under wind uncertainty is a critical safety issue for low-altitude air-ground delivery.<n>This paper proposes Energy-Aware Wind-Resilient Routing (EWR), an online risk-sensitive planning framework for wind-aware and energy-safe UAV routing.<n> Experiments using synthetic delivery graphs with replayed wind logs from a public truck-UAV delivery dataset show that EWR improves mission success rates and reduces wind-induced return failures.
  arXiv  Detail & Related papers  (2026-08-12T04:40:48Z)
• Wind-Informed Rapid Flight-Planning in Complex Urban Topologies via Machine Learning and Experimental Validation [46.65676221937358]
  This work proposes a novel framework towards safe flight planning of aerial vehicles in windy urban environments.<n>A learning-based surrogate model is trained to rapidly predict flow fields from readily available information.<n>A safe, flow-informed flight trajectory is then identified through a cost-minimizing pathfinder.
  arXiv  Detail & Related papers  (2026-08-10T23:30:49Z)
• Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability [79.08785366532287]
  We propose a lightweight risk-gated reinforcement learning approximation for risk-sensitive control under partial observability.<n>We evaluate the approach in two safety-critical partially observable domains: automated glucose regulation and safety-constrained navigation.
  arXiv  Detail & Related papers  (2026-05-14T01:23:09Z)
• Robust Energy-Aware Routing for Air-Ground Cooperative Multi-UAV Delivery in Wind-Uncertain Environments [8.00361595836773]
  We propose Battery-Efficient Routing (BER), an online risk-sensitive planning framework for wind-sensitive truck-assisted UAV delivery.<n>The problem is formulated as routing on a time dependent energy graph whose edge costs evolve according to wind-induced aerodynamic effects.<n>The approach is embedded in a hierarchical aerial-ground delivery architecture that combines task allocation, routing, and decentralized trajectory execution.
  arXiv  Detail & Related papers  (2026-04-15T03:43:18Z)
• PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments [0.0]
  We consider energy-aware planning for an unmanned aerial vehicle (UAV) team operating in an environment.<n>The UAV must visit a set of air points in minimum time while respecting energy constraints, relying on the UGV as a mobile charging station.<n>Unlike prior work that assumed deterministic travel times or used fixed margins, we model times as random variables and bound the probability of failure (energy depletion) across the entire mission to a user-specified risk level.
  arXiv  Detail & Related papers  (2026-04-02T15:13:40Z)
• Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments [0.46873264197900916]
  We propose a risk-aware approach that harnesses semantic segmentation to continuously evaluate potential hazards in the drone's field of view.<n>Our method adaptively identifies a stable Safe Landing Zone (SLZ) despite moving critical obstacles.<n>A control system then guides the UAV toward this low-risk region, employing altitude-dependent safety thresholds and temporal landing point stabilization.
  arXiv  Detail & Related papers  (2025-05-26T18:16:21Z)
• Leaky Batteries: A Novel Set of Side-Channel Attacks on Electric Vehicles [17.351539765989433]
  Current research has largely overlooked the broader implications of battery consumption data exposure. We introduce a novel class of side-channel attacks that exploit EV battery data to extract sensitive user information. Our attacks achieve an average success rate of 95.4% across all attack objectives.
  arXiv  Detail & Related papers  (2025-03-11T23:18:26Z)
• Research on Dangerous Flight Weather Prediction based on Machine Learning [4.352963801845065]
  The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather.
  arXiv  Detail & Related papers  (2024-06-18T06:08:15Z)
• Risk Management of Unmanned Aerial Vehicles [0.0]
  This paper presents an efficient risk management model for unmanned aerial vehicles or UAVs. The increasing use of UAVs or drones in many fields and the existence of different threats is the main reason to have an efficient risk management method for them.
  arXiv  Detail & Related papers  (2023-11-07T10:46:08Z)
• Perception-and-Energy-aware Motion Planning for UAV using Learning-based Model under Heteroscedastic Uncertainty [1.223779595809275]
  This study presents perception-and-energy-aware motion planning for UAVs in denied environments. A high-fidelity simulator acquires a flight dataset to learn energy consumption for the UAV and heteroscedastic uncertainty associated with LiDAR measurements. The learned models enable the online planner to estimate energy consumption and perception quality, reducing UAV battery usage and localization errors.
  arXiv  Detail & Related papers  (2023-09-25T16:34:54Z)
• A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness [52.27309191283943]
  This paper presents a data-driven framework for assessing the risk of different AVs' behaviors. We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision.
  arXiv  Detail & Related papers  (2023-08-02T09:48:08Z)
• Predicting Real-time Crash Risks during Hurricane Evacuation Using Connected Vehicle Data [1.3706331473063877]
  We present methods to determine potential crash risks during hurricane evacuation from an emerging alternative data source known as connected vehicle data. Multiple machine learning models were trained considering weather features and different traffic characteristics extracted from the connected vehicle data.
  arXiv  Detail & Related papers  (2023-06-14T18:04:07Z)
• To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs [3.6118662460334527]
  Deep reinforcement learning technique is presented for task offloading decision-making algorithms for a smart farm Internet of Things network. The technique uses financial concepts such as cost functions and conditional variable at risk (CVaR) in order to quantify the damage that may be caused by each risky action. The proposed CVaR-based technique was compared to other deep reinforcement learning techniques and two fixed rule-based techniques. The simulation results show that the CVaR-based risk method eliminated the most dangerous risk, which was exceeding the deadline for a fire detection task.
  arXiv  Detail & Related papers  (2023-02-14T23:50:37Z)
• E^2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles [78.50123964690094]
  Unmanned Aerial Vehicles (UAVs) based video text spotting has been extensively used in civil and military domains. Our proposed energy-efficient video text spotting solution, dubbed as E2VTS, outperforms all previous methods by achieving a competitive tradeoff between energy efficiency and performance.
  arXiv  Detail & Related papers  (2022-06-05T22:43:17Z)
• R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes [69.6715406227469]
  Self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches. We present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework.
  arXiv  Detail & Related papers  (2021-08-10T17:57:03Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings [129.80279257258098]
  Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous. We propose a "safety-critical adaptation" task setting: an agent first trains in non-safety-critical "source" environments. We propose a solution approach, CARL, that builds on the intuition that prior experience in diverse environments equips an agent to estimate risk.
  arXiv  Detail & Related papers  (2020-08-15T01:40:59Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.