Range, Endurance, and Optimal Speed Estimates for Multicopters
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Abstract (arXiv)
Multicopters are among the most versatile mobile robots. Their applications range from inspection and mapping tasks to providing vital reconnaissance in disaster zones and to package delivery. The range, endurance, and speed a multirotor vehicle can achieve while performing its task is a decisive factor not only for vehicle design and mission planning, but also for policy makers deciding on the rules and regulations for aerial robots. To the best of the authors' knowledge, this work proposes the first approach to estimate the range, endurance, and optimal flight speed for a wide variety of multicopters. This advance is made possible by combining a state-of-the-art first-principles aerodynamic multicopter model based on blade-element-momentum theory with an electric-motor model and a graybox battery model. This model predicts the cell voltage with only 1.3% relative error (43.1 mV), even if the battery is subjected to non-constant discharge rates. Our approach is validated with real-world experiments on a test bench as well as with flights at speeds up to 65 km/h in one of the world's largest motion-capture systems. We also present an accurate pen-and-paper algorithm to estimate the range, endurance and optimal speed of multicopters to help future researchers build drones with maximal range and endurance, ensuring that future multirotor vehicles are even more versatile.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Sep 2021.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 2.
6 papers.
- 2025TACO: General Acrobatic Flight Control via Target-and-Command-Oriented Reinforcement Learningcited by 3
- 2026Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Dronescited by 1
- 2023Vehicle Fault-Tolerant Robust Power Transmission Line Inspection Planningcited by 0
- 2024Airship Formations for Animal Motion Capture and Behavior Analysiscited by 0
- 2025HDVIO2.0: Wind and Disturbance Estimation with Hybrid Dynamics VIOcited by 0
- 2026Battery-Aware Reinforcement Learning for Aggressive Quadrotor Flightcited by 0
- 2026Battery-Aware Reinforcement Learning for Aggressive Quadrotor Flight29 Sep 2026
- 2026Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones24 Feb 2026
- 2025HDVIO2.0: Wind and Disturbance Estimation with Hybrid Dynamics VIO1 Apr 2025
- 2025TACO: General Acrobatic Flight Control via Target-and-Command-Oriented Reinforcement Learning3 Mar 2025
- 2024Airship Formations for Animal Motion Capture and Behavior Analysis13 Apr 2024
- 2023Vehicle Fault-Tolerant Robust Power Transmission Line Inspection Planning2 Feb 2023