Abstract
This review examines energy-aware latent policies for governed UAV operations through an evidence-centered design lens. The analysis asks how uncertainty and reserve constraints should shape path choice. It treats the relevant unit as a complete pathway from data or physical observations to representation, model output, human interpretation, and accountable action. The cited literature is synthesized without inventing experiments or unreported performance values. Particular attention is given to an optimized route exhausting recovery margin. The review argues that credible translation requires explicit evidence boundaries, uncertainty-aware evaluation, author-visible traceability, and a documented route for intervention. The resulting framework supports agricultural and urban UAV missions while distinguishing component promise from system readiness.
References
Beard, R. W., & McLain, T. W. (2012). Small unmanned aircraft: Theory and practice. Princeton University Press.
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. In Proceedings of the 26th International Conference on Machine Learning (pp. 41-48).
Deng, H., Luo, H., Zhu, Y., Li, L., Chen, Z., Zhao, X., Li, M., Zhang, J., Wang, M., Cao, Y., & Kang, Y. (2026). IIB-LPO: Latent policy optimization via iterative information bottleneck. arXiv preprint arXiv:2601.05870.
Floreano, D., & Wood, R. J. (2015). Science, technology and the future of small autonomous drones. Nature, 521, 460-466. https://doi.org/10.1038/nature14542
Kendoul, F. (2012). Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. Journal of Field Robotics, 29(2), 315-378. https://doi.org/10.1002/rob.20414
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
Tao, J., Lyu, R., & Cao, X. (2026). A Deep Learning-Based Automated Content Moderation Framework for Online Platforms. Future-Adaptive Intelligence and Lifelong Systems, 1(1).
Tishby, N., Pereira, F. C., & Bialek, W. (2000). The information bottleneck method. arXiv preprint physics/0004057.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Xu, M. (2026). Energy-Efficient UAV Path Planning for Maize Disease Monitoring via Generative Data Augmentation and Uncertainty-Guided Navigation. Smart Agricultural Technology, 102278.
