Autonomous UAV Navigation in Unfamiliar Indoor Environments Using Deep Reinforcement Learning
Keywords:
UAV navigation, Deep reinforcement learning, Adaptive path planning, Indoor autonomous system, AirSim simulationAbstract
Efficient and collision-free navigation in GPS-denied indoor environments remains a fundamental challenge in the development of intelligent Unmanned Aerial Vehicles (UAVs). This study introduces DRAL-UAV, a Deep Reinforcement Adaptive Learning framework tailored for path planning in partially observable environments. The proposed model features a hierarchical policy network that integrates a visual encoder, a recurrent memory module, and an adaptive reward adjustment mechanism. Training and evaluation are conducted using the AirSim 3D simulation platform, incorporating complex conditions such as dynamic obstacles and narrow corridors. Experimental results demonstrate that DRAL-UAV achieves an average navigation success rate of 92.6% over 5,000 trials across 100 scenarios, outperforming traditional A* and DDPG methods. Furthermore, the model reduces the average path length by 28.4% and maintains a low failure rate of 5% in challenging narrow corridor scenarios (corridor width ratio = 1.2). The finding highlight DRAL-UAV’s robust decision-making ability, improved generalization in unstructured environments and high applicability in real-world UAV tasks, including search and rescue, warehouse automation and industrial inspections.
References
Mo, K., Chu, L., Zhang, X., Su, X., Qian, Y., Ou, Y., & Pretorius, W. (2024). Dral: Deep reinforcement adaptive learning for multi-uavs navigation in unknown indoor environment. arXiv preprint arXiv: 2409. 03930.
Shih, K., Han, Y., & Tan, L. (2025). Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions.
Bao, Q., Chen, Y., & Ji, X. (2025). Research on evolution and early warning model of network public opinion based on online Latent Dirichlet distribution model and BP neural network. arXiv preprint arXiv: 2503. 03755.
Zhu, J., Wu, Y., Liu, Z., & Costa, C. (2025). Sustainable Optimization in Supply Chain Management Using Machine Learning. International Journal of Management Science Research, 8(1).
Vepa, A., Yang, Z., Choi, A., Joo, J., Scalzo, F., & Sun, Y. (2024). Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation. Advances in Neural Information Processing Systems, 37, 71643-71671.
Feng, H. (2024, September). The research on machine-vision-based EMI source localization technology for DCDC converter circuit boards. In Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024) (Vol. 13275, pp. 250-255). SPIE.
Liu, Z., Costa, C., & Wu, Y. (2024). Quantitative Assessment of Sustainable Supply Chain Practices Using Life Cycle and Economic Impact Analysis.
Yang, Z., & Zhu, Z. (2024). Curiousllm: Elevating multi-document qa with reasoning-infused knowledge graph prompting. arXiv preprint arXiv: 2404. 09077.
Zhu, J., Ortiz, J., & Sun, Y. (2024, November). Decoupled Deep Reinforcement Learning with Sensor Fusion and Imitation Learning for Autonomous Driving Optimization. In 2024 6th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 306-310). IEEE.
Shi, X., Tao, Y., & Lin, S. C. (2024, November). Deep Neural Network-Based Prediction of B-Cell Epitopes for SARS-CoV and SARS-CoV-2: Enhancing Vaccine Design through Machine Learning. In 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM) (pp. 259-263). IEEE.
Zhu, J., Xu, T., Zhang, Y., & Fan, Z. (2024). Scalable Edge Computing Framework for Real-Time Data Processing in Fintech Applications. International Journal of Advance in Applied Science Research, 3, 85-92.
Wang, S., Jiang, R., Wang, Z., & Zhou, Y. (2024). Deep learning-based anomaly detection and log analysis for computer networks. arXiv preprint arXiv: 2407. 05639.
Gong, C., Zhang, X., Lin, Y., Lu, H., Su, P. C., & Zhang, J. (2025). Federated Learning for Heterogeneous Data Integration and Privacy Protection.
Li, Z., Ji, Q., Ling, X., & Liu, Q. (2025). A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games. Authorea Preprints.
Zhang, W., Li, Z., & Tian, Y. (2025). Research on Temperature Prediction Based on RF-LSTM Modeling. Authorea Preprints.
Zhu, J., Xu, T., Liu, M., & Chen, C. (2024). Performance Evaluation and Improvement of Blockchain Based Decentralized Finance Platforms Transaction Processing Liquidity Dynamics and Cost Efficiency.
Li, Z. (2024). Advances in Deep Reinforcement Learning for Computer Vision Applications. Journal of Industrial Engineering and Applied Science, 2(6), 16-26.
Liu, J., Li, K., Zhu, A., Hong, B., Zhao, P., Dai, S., ... & Su, H. (2024). Application of deep learning-based natural language processing in multilingual sentiment analysis. Mediterranean Journal of Basic and Applied Sciences (MJBAS), 8(2), 243-260.
Liu, Z., Costa, C., & Wu, Y. (2024). Leveraging Data-Driven Insights to Enhance Supplier Performance and Supply Chain Resilience.
Tang, X., Wang, Z., Cai, X., Su, H., & Wei, C. (2024, August). Research on heterogeneous computation resource allocation based on data-driven method. In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS) (pp. 916-919). IEEE.
Feng, H. (2024). High-Efficiency Dual-Band 8-Port MIMO Antenna Array for Enhanced 5G Smartphone Communications. Journal of Artificial Intelligence and Information, 1, 71-78.
Zhu, J., Sun, Y., Zhang, Y., Ortiz, J., & Fan, Z. (2024, October). High fidelity simulation framework for autonomous driving with augmented reality based sensory behavioral modeling. In IET Conference Proceedings CP989 (Vol. 2024, No. 21, pp. 670-674). Stevenage, UK: The Institution of Engineering and Technology.
Liu, Z., Costa, C., & Wu, Y. (2024). Data-Driven Optimization of Production Efficiency and Resilience in Global Supply Chains. Journal of Theory and Practice of Engineering Science, 4(08), 23-33.
Sun, Y., Pai, N., Ramesh, V. V., Aldeer, M., & Ortiz, J. (2023). GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces. arXiv preprint arXiv: 2306. 15857.
Narumi, K., Qin, F., Liu, S., Cheng, H. Y., Gu, J., Kawahara, Y., ... & Yao, L. (2019, October). Self-healing UI: Mechanically and electrically self-healing materials for sensing and actuation interfaces. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (pp. 293-306).
Gu, J., Narayanan, V., Wang, G., Luo, D., Jain, H., Lu, K., ... & Yao, L. (2020, November). Inverse design tool for asymmetrical self-rising surfaces with color texture. In Proceedings of the 5th Annual ACM Symposium on Computational Fabrication (pp. 1-12).
Wang, Z., Yan, H., Wei, C., Wang, J., Bo, S., & Xiao, M. (2024, August). Research on autonomous driving decision-making strategies based deep reinforcement learning. In Proceedings of the 2024 4th International Conference on Internet of Things and Machine Learning (pp. 211-215).
Lee, C. C., & Song, K. T. (2023). Path re-planning design of a cobot in a dynamic environment based on current obstacle configuration. IEEE Robotics and Automation Letters, 8(3), 1183-1190.
Rajasekhar, N., Radhakrishnan, T. K., & Samsudeen, N. (2025). Exploring reinforcement learning in process control: a comprehensive survey. International Journal of Systems Science, 1-30.
Zeng, J., Ju, R., Qin, L., Hu, Y., Yin, Q., & Hu, C. (2019). Navigation in unknown dynamic environments based on deep reinforcement learning. Sensors, 19(18), 3837.
Kamil, F., Tang, S. H., Khaksar, W., Zulkifli, N., & Ahmad, S. A. (2015). A review on motion planning and obstacle avoidance approaches in dynamic environments. Advances in Robotics & Automation, 4(2), 134-142.
