Performance Analysis of AI tools for Drone Endurance Optimization

DOI

10.2991/978-94-6239-616-6_36How to use a DOI?

Keywords

Drone Endurance Optimization; Artificial Intelligence (AI); Machine learning; UAV

Abstract

The intensive development of drone technology has increased the pressure on the long-term flight time, an essential parameter that determines the effectiveness of actions in different fields of application: surveillance, agricultural activities, logistics, and disaster management. The paper examines the potential of integrating Artificial Intelligence (AI) technologies in improving the endurance of unmanned aerial vehicles (UAVs) with intelligent flight planning, management of energy, and adaptive control systems. Machine learning algorithms, such as Random Forest and XG-Boost are used, to forecast energy consumption and obtain the best flight parameters. Genetic Algorithms (GA) to find the best combinations of payload, speed, and battery usage are used. The reinforcement Learning (RL) enabled real-time adaptive planning of paths in various circumstances of the environment. Using structured UAV data, AI-based optimization presented major improvements in the flight time (up to 26.7%), energy (up to 22.5% reduction in Wh/km) and battery consumption (up to 13.3% increase). The RL agent outperformed other models in terms of the best predictive accuracy (R 2 = 0.97). Overall, AI-based approaches can significantly increase the lifespan of UAVs, which provides a beneficial model of drone use that can be efficient and sustainable.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Durishetti Sathish Kumar
AU  - R. Sundaramurthy
AU  - M. Florance Mary
AU  - B. HemaKumar
PY  - 2026
DA  - 2026/03/31
TI  - Performance Analysis of AI tools for Drone Endurance Optimization
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 474
EP  - 490
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_36
DO  - 10.2991/978-94-6239-616-6_36
ID  - Kumar2026
ER  -